diff --git "a/run-output/training/train_grpo.executed.ipynb" "b/run-output/training/train_grpo.executed.ipynb"
--- "a/run-output/training/train_grpo.executed.ipynb"
+++ "b/run-output/training/train_grpo.executed.ipynb"
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5ca314db",
+   "id": "7fb6ceb6",
    "metadata": {
     "papermill": {
-     "duration": 0.003358,
-     "end_time": "2026-04-25T22:47:20.131387+00:00",
+     "duration": 0.003387,
+     "end_time": "2026-04-26T00:59:12.340616+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:20.128029+00:00",
+     "start_time": "2026-04-26T00:59:12.337229+00:00",
      "status": "completed"
     },
     "tags": []
@@ -36,19 +36,19 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "b5d8dbe9",
+   "id": "f7c249c4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:47:20.137930Z",
-     "iopub.status.busy": "2026-04-25T22:47:20.137724Z",
-     "iopub.status.idle": "2026-04-25T22:47:50.522798Z",
-     "shell.execute_reply": "2026-04-25T22:47:50.521941Z"
+     "iopub.execute_input": "2026-04-26T00:59:12.346783Z",
+     "iopub.status.busy": "2026-04-26T00:59:12.346590Z",
+     "iopub.status.idle": "2026-04-26T00:59:45.329611Z",
+     "shell.execute_reply": "2026-04-26T00:59:45.328853Z"
     },
     "papermill": {
-     "duration": 30.389206,
-     "end_time": "2026-04-25T22:47:50.523434+00:00",
+     "duration": 32.98719,
+     "end_time": "2026-04-26T00:59:45.330490+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:20.134228+00:00",
+     "start_time": "2026-04-26T00:59:12.343300+00:00",
      "status": "completed"
     },
     "tags": []
@@ -93,33 +93,83 @@
       "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\r\n",
       "\u001b[0m"
      ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\r\n",
+      "  \r\n",
+      "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\r\n",
+      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\r\n",
+      "  \u001b[31m╰─>\u001b[0m \u001b[31m[21 lines of output]\u001b[0m\r\n",
+      "  \u001b[31m   \u001b[0m \r\n",
+      "  \u001b[31m   \u001b[0m \r\n",
+      "  \u001b[31m   \u001b[0m torch.__version__  = 2.5.1+cu124\r\n",
+      "  \u001b[31m   \u001b[0m \r\n",
+      "  \u001b[31m   \u001b[0m \r\n",
+      "  \u001b[31m   \u001b[0m /tmp/pip-install-ldsack7d/flash-attn_09e25160e4bf4bbcbdb088cf5fe85bc6/setup.py:106: UserWarning: flash_attn was requested, but nvcc was not found.  Are you sure your environment has nvcc available?  If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, only images whose names contain 'devel' will provide nvcc.\r\n",
+      "  \u001b[31m   \u001b[0m   warnings.warn(\r\n",
+      "  \u001b[31m   \u001b[0m Traceback (most recent call last):\r\n",
+      "  \u001b[31m   \u001b[0m   File \"<string>\", line 2, in <module>\r\n",
+      "  \u001b[31m   \u001b[0m   File \"<pip-setuptools-caller>\", line 34, in <module>\r\n",
+      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-install-ldsack7d/flash-attn_09e25160e4bf4bbcbdb088cf5fe85bc6/setup.py\", line 227, in <module>\r\n",
+      "  \u001b[31m   \u001b[0m     CUDAExtension(\r\n",
+      "  \u001b[31m   \u001b[0m   File \"/opt/conda/lib/python3.11/site-packages/torch/utils/cpp_extension.py\", line 1078, in CUDAExtension\r\n",
+      "  \u001b[31m   \u001b[0m     library_dirs += library_paths(cuda=True)\r\n",
+      "  \u001b[31m   \u001b[0m                     ^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  \u001b[31m   \u001b[0m   File \"/opt/conda/lib/python3.11/site-packages/torch/utils/cpp_extension.py\", line 1209, in library_paths\r\n",
+      "  \u001b[31m   \u001b[0m     if (not os.path.exists(_join_cuda_home(lib_dir)) and\r\n",
+      "  \u001b[31m   \u001b[0m                            ^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+      "  \u001b[31m   \u001b[0m   File \"/opt/conda/lib/python3.11/site-packages/torch/utils/cpp_extension.py\", line 2416, in _join_cuda_home\r\n",
+      "  \u001b[31m   \u001b[0m     raise OSError('CUDA_HOME environment variable is not set. '\r\n",
+      "  \u001b[31m   \u001b[0m OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.\r\n",
+      "  \u001b[31m   \u001b[0m \u001b[31m[end of output]\u001b[0m\r\n",
+      "  \r\n",
+      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\r\n",
+      "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\r\n",
+      "\r\n",
+      "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\r\n",
+      "\u001b[31m╰─>\u001b[0m See above for output.\r\n",
+      "\r\n",
+      "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\r\n",
+      "\u001b[1;36mhint\u001b[0m: See above for details.\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "flash-attn install skipped; will use sdpa\r\n"
+     ]
     }
    ],
    "source": [
     "# Cell 1: Install dependencies (quote versions — zsh treats `>` as redirect otherwise)\n",
     "!pip install -q torch torchvision torchaudio\n",
-    "!pip install -q \"transformers>=4.45.0\" \"accelerate\" \"peft>=0.10.0\" \"trl>=0.20.0\" \"datasets\" \"bitsandbytes\"\n",
+    "!pip install -q \"transformers>=4.45.0\" \"accelerate\" \"peft>=0.10.0\" \"trl>=0.20.0\" \"datasets\"\n",
     "!pip install -q matplotlib pandas\n",
     "!pip install -q \"typing_extensions>=4.13.0\" pydantic httpx\n",
-    "!pip install -q \"openenv-core[core]>=0.2.2\""
+    "!pip install -q \"openenv-core[core]>=0.2.2\"\n",
+    "!pip install -q flash-attn --no-build-isolation || echo \"flash-attn install skipped; will use sdpa\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a7e61bef",
+   "id": "730e7827",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:47:50.530520Z",
-     "iopub.status.busy": "2026-04-25T22:47:50.530325Z",
-     "iopub.status.idle": "2026-04-25T22:47:50.539949Z",
-     "shell.execute_reply": "2026-04-25T22:47:50.539185Z"
+     "iopub.execute_input": "2026-04-26T00:59:45.337978Z",
+     "iopub.status.busy": "2026-04-26T00:59:45.337763Z",
+     "iopub.status.idle": "2026-04-26T00:59:45.347380Z",
+     "shell.execute_reply": "2026-04-26T00:59:45.346713Z"
     },
     "papermill": {
-     "duration": 0.013765,
-     "end_time": "2026-04-25T22:47:50.540478+00:00",
+     "duration": 0.013887,
+     "end_time": "2026-04-26T00:59:45.347864+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:50.526713+00:00",
+     "start_time": "2026-04-26T00:59:45.333977+00:00",
      "status": "completed"
     },
     "tags": []
@@ -132,8 +182,8 @@
       "Mode: local\n",
       "Repo root: /work\n",
       "Working dir: /work\n",
-      "Branch: hack1\n",
-      "Commit: 4bfe286\n",
+      "Branch: main\n",
+      "Commit: f7b5241\n",
       "Plots dir: /work/plots\n"
      ]
     }
@@ -146,7 +196,7 @@
     "import subprocess\n",
     "from pathlib import Path\n",
     "\n",
-    "REPO_BRANCH = \"hack1\"\n",
+    "REPO_BRANCH = \"main\"\n",
     "REPO_URL = \"https://github.com/VaibhavKhandare/viral-posts-env.git\"\n",
     "COLAB_REPO = Path(\"/content/viral-posts-env\")\n",
     "\n",
@@ -218,19 +268,19 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "f4bc414f",
+   "id": "a3fdb3e9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:47:50.547817Z",
-     "iopub.status.busy": "2026-04-25T22:47:50.547614Z",
-     "iopub.status.idle": "2026-04-25T22:47:55.317710Z",
-     "shell.execute_reply": "2026-04-25T22:47:55.316949Z"
+     "iopub.execute_input": "2026-04-26T00:59:45.354510Z",
+     "iopub.status.busy": "2026-04-26T00:59:45.354359Z",
+     "iopub.status.idle": "2026-04-26T00:59:49.845266Z",
+     "shell.execute_reply": "2026-04-26T00:59:49.844498Z"
     },
     "papermill": {
-     "duration": 4.774891,
-     "end_time": "2026-04-25T22:47:55.318265+00:00",
+     "duration": 4.495027,
+     "end_time": "2026-04-26T00:59:49.845862+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:50.543374+00:00",
+     "start_time": "2026-04-26T00:59:45.350835+00:00",
      "status": "completed"
     },
     "tags": []
@@ -241,7 +291,7 @@
      "output_type": "stream",
      "text": [
       "GPU: NVIDIA L40S\n",
-      "Tags: 114, Topics: 100, Horizon: 7 days\n",
+      "Tags: 114, Topics: 100, Horizon: 15 days\n",
       "OK: ast.parse (syntax check)\n"
      ]
     }
@@ -289,6 +339,12 @@
     "print(f\"GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU'}\")\n",
     "print(f\"Tags: {len(TAG_POOL)}, Topics: {len(ALL_TOPICS)}, Horizon: {TASK_HORIZON} days\")\n",
     "\n",
+    "# Hard stop if stale repo/code is loaded\n",
+    "assert TASK_HORIZON == 15, (\n",
+    "    f\"Expected TASK_HORIZON=15, got {TASK_HORIZON}. \"\n",
+    "    \"Restart runtime and run from Cell 1 again (clean clone on main).\"\n",
+    ")\n",
+    "\n",
     "# Same sanity as syntax_only.ipynb (kernel parses modern Python)\n",
     "import ast\n",
     "ast.parse(\"def _t(x: int) -> str: return f'{x}'\")\n",
@@ -297,13 +353,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5fa33870",
+   "id": "0b65ccf3",
    "metadata": {
     "papermill": {
-     "duration": 0.002762,
-     "end_time": "2026-04-25T22:47:55.325185+00:00",
+     "duration": 0.003827,
+     "end_time": "2026-04-26T00:59:49.864630+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:55.322423+00:00",
+     "start_time": "2026-04-26T00:59:49.860803+00:00",
      "status": "completed"
     },
     "tags": []
@@ -317,19 +373,19 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "077a1629",
+   "id": "b815e3ce",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:47:55.334315Z",
-     "iopub.status.busy": "2026-04-25T22:47:55.333882Z",
-     "iopub.status.idle": "2026-04-25T22:47:55.343276Z",
-     "shell.execute_reply": "2026-04-25T22:47:55.342442Z"
+     "iopub.execute_input": "2026-04-26T00:59:49.871675Z",
+     "iopub.status.busy": "2026-04-26T00:59:49.871259Z",
+     "iopub.status.idle": "2026-04-26T00:59:49.880275Z",
+     "shell.execute_reply": "2026-04-26T00:59:49.879588Z"
     },
     "papermill": {
-     "duration": 0.01574,
-     "end_time": "2026-04-25T22:47:55.343789+00:00",
+     "duration": 0.013255,
+     "end_time": "2026-04-26T00:59:49.880896+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:55.328049+00:00",
+     "start_time": "2026-04-26T00:59:49.867641+00:00",
      "status": "completed"
     },
     "tags": []
@@ -423,19 +479,19 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "2a4c6d33",
+   "id": "88816683",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:47:55.350265Z",
-     "iopub.status.busy": "2026-04-25T22:47:55.350091Z",
-     "iopub.status.idle": "2026-04-25T22:47:55.385312Z",
-     "shell.execute_reply": "2026-04-25T22:47:55.384426Z"
+     "iopub.execute_input": "2026-04-26T00:59:49.887859Z",
+     "iopub.status.busy": "2026-04-26T00:59:49.887678Z",
+     "iopub.status.idle": "2026-04-26T00:59:49.950323Z",
+     "shell.execute_reply": "2026-04-26T00:59:49.949658Z"
     },
     "papermill": {
-     "duration": 0.039231,
-     "end_time": "2026-04-25T22:47:55.385903+00:00",
+     "duration": 0.066873,
+     "end_time": "2026-04-26T00:59:49.950886+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:55.346672+00:00",
+     "start_time": "2026-04-26T00:59:49.884013+00:00",
      "status": "completed"
     },
     "tags": []
@@ -451,31 +507,31 @@
       "   always_rest |      monthly_strategic | score=0.1750 | energy=1.00\n",
       "   always_rest |    monthly_competitive | score=0.0350 | energy=1.00\n",
       "\n",
-      "          spam |         monthly_engage | score=0.0178 | energy=0.00\n",
+      "          spam |         monthly_engage | score=0.0081 | energy=0.00\n",
       "          spam |      monthly_strategic | score=0.0075 | energy=0.00\n",
       "          spam |    monthly_competitive | score=0.0000 | energy=0.00\n",
       "\n",
-      "        random |         monthly_engage | score=0.6125 | energy=1.00\n",
-      "        random |      monthly_strategic | score=0.6080 | energy=1.00\n",
-      "        random |    monthly_competitive | score=0.6825 | energy=1.00\n",
+      "        random |         monthly_engage | score=0.5131 | energy=1.00\n",
+      "        random |      monthly_strategic | score=0.6160 | energy=1.00\n",
+      "        random |    monthly_competitive | score=0.6611 | energy=1.00\n",
       "\n",
-      "       minimal |         monthly_engage | score=0.3349 | energy=1.00\n",
-      "       minimal |      monthly_strategic | score=0.4813 | energy=1.00\n",
-      "       minimal |    monthly_competitive | score=0.3251 | energy=1.00\n",
+      "       minimal |         monthly_engage | score=0.3560 | energy=1.00\n",
+      "       minimal |      monthly_strategic | score=0.6411 | energy=1.00\n",
+      "       minimal |    monthly_competitive | score=0.3509 | energy=1.00\n",
       "\n",
-      "         smart |         monthly_engage | score=0.6342 | energy=1.00\n",
-      "         smart |      monthly_strategic | score=0.7218 | energy=1.00\n",
-      "         smart |    monthly_competitive | score=0.8315 | energy=1.00\n",
+      "         smart |         monthly_engage | score=0.7352 | energy=1.00\n",
+      "         smart |      monthly_strategic | score=0.9043 | energy=1.00\n",
+      "         smart |    monthly_competitive | score=0.9066 | energy=1.00\n",
       "\n",
       "\n",
       "LEADERBOARD\n",
       "Agent              Engage    Strategic    Competitive      Avg\n",
       "------------------------------------------------------------\n",
       "always_rest        0.0000       0.1750         0.0350   0.0700\n",
-      "spam               0.0178       0.0075         0.0000   0.0084\n",
-      "random             0.6125       0.6080         0.6825   0.6343\n",
-      "minimal            0.3349       0.4813         0.3251   0.3804\n",
-      "smart              0.6342       0.7218         0.8315   0.7292\n"
+      "spam               0.0081       0.0075         0.0000   0.0052\n",
+      "random             0.5131       0.6160         0.6611   0.5967\n",
+      "minimal            0.3560       0.6411         0.3509   0.4493\n",
+      "smart              0.7352       0.9043         0.9066   0.8487\n"
      ]
     }
    ],
@@ -518,19 +574,19 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "5e1c14be",
+   "id": "6ac4e9b0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:47:55.392614Z",
-     "iopub.status.busy": "2026-04-25T22:47:55.392439Z",
-     "iopub.status.idle": "2026-04-25T22:47:55.784484Z",
-     "shell.execute_reply": "2026-04-25T22:47:55.783650Z"
+     "iopub.execute_input": "2026-04-26T00:59:49.957810Z",
+     "iopub.status.busy": "2026-04-26T00:59:49.957642Z",
+     "iopub.status.idle": "2026-04-26T00:59:50.338510Z",
+     "shell.execute_reply": "2026-04-26T00:59:50.337742Z"
     },
     "papermill": {
-     "duration": 0.396079,
-     "end_time": "2026-04-25T22:47:55.785041+00:00",
+     "duration": 0.385016,
+     "end_time": "2026-04-26T00:59:50.339004+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:55.388962+00:00",
+     "start_time": "2026-04-26T00:59:49.953988+00:00",
      "status": "completed"
     },
     "tags": []
@@ -538,7 +594,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABjIAAAHvCAYAAAD+XUa3AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAjNlJREFUeJzs3Xd4FFX7//FPOumFQEJISKgxYCARUCGUCAiKIE2kQ0BRRAKIWOiCSFMQBJVHpAgP0pSqggIPHQkiBhQUEUFEmrSEEFrI/P7gl/lmSSGBlCW8X9e117U758zMmdlk79m955xjYxiGIQAAAAAAAAAAACtkW9gNAAAAAAAAAAAAyAqJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBqkcgAAAAAAAAAAABWi0QGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAcM+IiYmRjY2NbGxsFB0dXSD73Lhxo7lPGxsbHTlypED2C+RGSEiI+Tf61ltv5eu+5syZY/E/gZzJ7vMr/fmcM2dOobSvKCqsz+/CiFUFjdgIAAAKGokMAABQYJ544gnzRw9vb29dvXo103qGYah8+fJm3cjIyAJuac5Y0w85Bf1D6MGDB/X++++rZcuWqly5snx8fOTo6KhSpUqpefPmWrlyZb634U6l/9E/qx8Zo6OjzTohISEF2r7CZC1Jilv/t9IednZ28vT0VLVq1dSnTx/9/vvvhdbG+8mt7wfJFgAAABQ0+8JuAAAAuH/ExMTo22+/lSRduHBBX331ldq0aZOh3rZt2/Tnn39arCdJ7du314MPPihJCgoKyv8GI0sTJ07Uf/7znwzLT548qa+++kpfffWVevfurQ8//LAQWnf/GTJkiBISEiRJtWvXztd91axZU++++26+7iMrqampSkxM1N69e7V3717Nnj1bGzduVM2aNQulPXkl/fm8148FAAAAyA8kMgAAQIFp2bKlvLy8dOHCBUnS3LlzM01kzJ0713zu4OCgTp06SbrZo+OJJ57I9X4TExPl4eFxZ41GtsqWLasnnnhCAQEB2r9/vxYvXqwbN25Ikj766CO1atVKjRo1KuRWFl0XL16Uu7u7evbsWWD7rFKliqpUqVJg+5Okdu3aqUaNGkpJSdHOnTu1bNkySVJycrLeeecdLV++vEDbk9cGDhxY2E1AHigqsaaoHAcAAChaGFoKAAAUmGLFiql9+/bm69WrV+vs2bMWda5evaolS5aYr5s1ayZfX19JuRtjfsWKFapdu7bc3NxUpkwZSdK5c+f0+uuvq2HDhgoJCZG7u7scHR3l5+enxx9/XPPmzZNhGDk6FhsbGz322GMWy8qWLWu2Ia0XSZo9e/aoR48eKl++vJydneXm5qbIyEiNGTNGly5dyrD9v/76Sy+++KIqVqwoZ2dnFStWTKVLl1ZUVJQGDBigX3/9VdL/DYGUXvfu3XM8LNLMmTPNuq6urhnacuHCBRUrVsysM3/+fEnSgw8+qFWrVunQoUP66KOPNHToUH3++eeaMWOGxfqrV6/Odv/3qqtXr2ratGmqV6+exbBabdu21ffff5+h/ltvvZXle3LkyBGLv9+NGzdmud7Zs2f18ssvKzAwUHZ2dpo5c6ak7OfIWLlypZ544gn5+fnJwcFBHh4eKl++vFq2bKmxY8cqNTXVbEP37t0t1k3frrTt3m74qZSUFM2aNUuNGzeWn5+fHB0dVaJECT366KMaOXJk7k+2biYxBw4cqDfffFNLly41e2ZJ0m+//WZRNz4+Xr1799Yjjzyi0qVLm/8/wcHBateunbZu3ZppmydPnqxatWrJy8tL9vb2Kl68uKpUqaKuXbtq4cKFGdY5deqUBg8erIiICLm7u6tYsWKqUKGCXn75ZR09ejRXx5fVsE23nuurV6/qnXfeUaVKleTk5KTAwEANHDgwy2H6Vq1apRYtWqhUqVJydHSUt7e3GjRooPnz5+f4s+5u5PYc3cl7J0lnz55Vr1695OfnJ2dnZ9WoUUOLFi26bftSU1M1b948NW7cWCVLljT/Vp966il98803GerfOsTWH3/8offee09hYWFycnJS165dM93P6dOn9fzzz8vf31/FihXTQw89lOnflCRdvnxZ77//vqKiouTt7W3GqKZNm2rx4sUZ6qekpGjYsGFq2rSpypcvLy8vLzk4OKh48eKqW7eupk6dquvXr1usk9lnzsyZM/XQQw/J2dlZ9erVu+tzCwAAkOcMAACAArRjxw5DkvmYNm2aRfmSJUssylesWGGWdevWzVxev359i/XSr1O3bl2L156enoZhGMbPP/9ssTyzR/fu3S22u2HDBovyw4cPZ9hfZo9u3bqZ2/joo48Me3v7LOtWrlzZOHHihFn/1KlTRokSJbLd/scff2wYhmHUr18/23rBwcHZvh+JiYmGi4uLWf/zzz+3KJ85c6bFeUxOTs52excvXrTYf58+fbKtXxiCg4Oz/DtKk/683noOT58+bURERGR5zm1tbY3JkydbrDNixIgst3f48GGL9Tds2JDper6+vsYDDzxgUff999/PcEwjRoww1589e/Zt/1YvX76coQ2ZPdK2e+s20zt79qxRs2bNLLeR9r94O7f+382ePdswDMNISUkxvv/+e8PDwyPL93Dq1KnZHoeNjY25vTTpP1syezzyyCMW9bdv3274+vpme5ybN2/Och/ZfX6lb9ut57pOnTqZ7q9Lly4W27tx44bRpUuXbI+pbdu2RkpKyl29H9m5k3N0J+/d+fPnM/xfpD2eeuopi9dpn9+GYRjJyclGo0aNst3fgAEDsj0Pt8aaFi1aGIZh+V5XrlzZCAkJyXT7EydOtNj+iRMnjCpVqmTbpjZt2hjXr18317n1MzezR6NGjSze61v/3289jmrVqt3VuQUAAMgPDC0FAAAK1COPPKKwsDCzR8HcuXP18ssvm+Xph5UqWbKkmjZtmut9bNmyRb6+vmrfvr2KFy+uffv2SZJsbW0VFhamhx9+WP7+/vLy8tKVK1f0008/adWqVTIMQ7Nnz1avXr308MMPZ7uPd999V4cOHdL06dPNZYMHD5a3t7ckmXeMb9++XX369FFqaqok6dFHH9UTTzyhixcv6rPPPtOZM2e0f/9+de3aVd99950k6csvv9S///4rSfL29lb37t1VvHhxHT9+XL/99pu2bNli7vOll15Ss2bN9Nprr5nL0obhkSRPT89sj8Pd3V3PPPOMed4///xzdejQwSz//PPPzeft27eXs7Nzttu79e74253Hwvb333/rvffey3R5Vrp06aL4+HhJN89fx44dFRgYqG3btmnNmjVKTU3VK6+8oho1aigqKirP2nrmzBmdOXNGjRo1UlRUlP7991/5+fllu87HH39sPq9Zs6aaNWumlJQU/f3334qLizP/D318fPTuu+9q165dFndbp5+7ISdzb3Tp0kU//PCD+TosLExNmzaVk5OTfvrpJ8XFxeX4eNPr3r17ht4i0s3/6fR/+5Lk5OSkRx99VBERESpevLjc3NyUkJCg9evX64cffpBhGHr11VfVrl07OTs7KykpSf/973/N9du0aaOHHnpICQkJ+uuvv7Rp0yaL7ScmJqply5Y6c+aMJJm9BZydnfXFF19o3759SkhIUJs2bXTw4MHb/g/mxtatW9WqVStVrlxZ8+fP15EjRyRJ8+fP17hx4xQQECBJmjBhgubNmyfpZm+PNm3aqFq1ajp8+LDmzZun69eva8mSJYqIiNDgwYPzrH1p7vQc5fa9k6ShQ4dafO7Ur19f9evX17Zt2/T1119n2cZXXnlF69atkyQ5Ojqqffv2qlixon7++WctWbJEhmFo0qRJql69ujp27JjpNrZs2aIqVaqoefPmMgxDdnZ2Gers379fnp6eeuWVV2RjY6NZs2aZwyu++eabevrpp1WhQgVJUqdOncx4JUnPPPOMKleurLVr15o9vb788kuNGTNGw4cPl3Tz/S1XrpweffRRlS5dWt7e3rp+/bp+++03LVmyRCkpKVq3bp2+/PJLPfvss1keR3BwsNq0aSMXFxedPn36rs4tAABAvijUNAoAALgvjR8/3uJOzgMHDhiGYRj//vuv4eDgYC5/5ZVXLNbL6R3NHh4exl9//ZXl/v/66y/jiy++MKZNm2a89957xrvvvmuULl3aXH/UqFFm3ax6ZNyuLE2rVq3M8ujoaOPGjRtm2c6dOy3W37Nnj2EYhjFp0iRz2Ysvvphhm0lJScbJkyezPP6c3C2d3saNG811HRwcjLNnzxqGcfPuYDs7O7MsLi4u2+1cvHjR4m78Bx54wLhy5Uqu2lIQ0vdeyMkjfQ+KPXv2WJT973//s9h206ZNzbJWrVqZy/OiR4Yko3///rc9pvQ9MqpWrWou//777zOsd/jwYYu/yex6W9yuzt69ey2WN23a1Lh27ZrFuocOHcp0m7e69X8rq8eYMWOy3MaePXuM//73v8aUKVOMd9991xg9erTFumm9Ac6dO2fx2XH16lWL7aSmphp//vmn+XrKlClmfW9vb/P/xTBu/m+m7001ZcoUsywvemSkf//j4+MtylauXGkYxs3eGOl7QgwfPtxiXxMmTDDLihcvbvH+ZyW3PTLu9Bylyel7d/36dcPNzc1cXq9ePfN4UlNTjcaNG2f6GX327FmLXnKzZs2y2H/v3r3NssjIyCzPw6OPPmpcvnw5Q/tv7eGzbds2s2zbtm0WZUOGDDEMwzB++ukni+Wvv/66uU5KSopRq1Yts8zHxyfD+3bq1CljxYoVxkcffWTGtQcffNBcp0ePHmbdWz9zypYta5w/f95ie3d6bgEAAPILPTIAAECB69KliwYPHmxOCj1v3jy9/fbbWrhwocVY3pndgZ0TXbt2NefFSO/s2bPq1q3bbe8kPXbs2B3tNzPbtm0zn2/cuDHTO3bTbN++XVWrVlVUVJRsbGxkGIb+85//6IcfflDlypUVGhqqGjVq6LHHHrvtnfi5Ua9ePZUvX16HDh3S9evX9eWXX6pnz54WE3dXqVIl294VJ0+e1NNPP23ejR8QEKBVq1bJyckpR21YtGhRtr0gcuLBBx+8o8ngcyP9+ylJDRo0yLLu9u3b83z/Q4cOzVX9unXrau/evZKkxx9/XLVq1VLFihVVuXJl1atXT+Hh4XnWtlvnLxgxYoQcHBwslpUrV+6Otp3Wy+jGjRvat2+fFixYoJSUFA0ePFjXr183706XpN27d6tr164Wd7ZnJu3/3NvbW1WqVNG+ffuUmJiosmXLqmbNmqpYsaLCw8PVsGFDlS1b1lwv/d/A+fPnVbx48Sz3sX37dvXt2/eOjjkzvXv3Np+HhoZalJ0/f16SdODAAbMnhCSNGjVKo0aNynR7Z8+e1e+//64HHnggz9oo3fk5yu1799tvvykpKclc3qFDB9na3pwG0sbGRp06dTJ7uqUXFxenlJQU83WPHj3Uo0ePTPcVHx+v5ORkubi4ZCgbOHCgihUrlm1by5UrZ9GbqXbt2ipbtqwOHz4sSfrxxx8lKcPcOt26dTOf29nZqXPnzmadc+fO6cCBAwoLC9Ply5fVu3dvzZ071+z5l5ns4trLL78sLy8vi2V3em4BAADyC4kMAABQ4EqVKqXGjRubE0H/97//1ahRoyyGlXrooYfu+EfWrH6Ue+6553I0HEZWE+feiXPnzuW4btpwUg8//LAmTZqkYcOGKSkpSbt379bu3bvNer6+vlqyZEmGCc/vVNrk5MOGDZN0czipnj17WgwrlV1S6eeff1azZs3MyXvLly+vb7/9VuXLl89xGz7++OMMQ/jkVrdu3XKdyKhfv77F5NppoqOjM23PnbyftzJumWQ5p39vvr6+2f4gnJkxY8bozz//1OrVq5WUlKS1a9dq7dq1Znn9+vX19ddfy9XVNVfbzcyt5yb9j/9364knnlBMTIz5uly5cubE4W+//baee+45lS5dWpcvX1azZs104sSJ224z/XlPG1Jt//79On78uFasWGGW2draql+/fpo0aZKkvPkbuFPpJ4q/NUmY9iN2bton3WxjXicy7uQc3cl7lzZEU5qSJUtavM4q4Zub9hmGobNnz2aayMjJebu1TWntSktkpB3DrW26te23vk5LXA0aNMhigvisZPc5k9lx3Om5BQAAyC8kMgAAQKGIiYkxExlHjhzRjBkzLMbWT/+jZW5l9qPspUuX9NVXX5mvGzZsqE8++UTBwcGys7PTww8/bLH/vOLj42OON16nTh21aNEiy7rp79rt37+/XnjhBe3YsUP79u3TwYMHtWbNGh08eFBnzpxRt27d9Ndff+VZO7t166YRI0YoNTVVmzdv1tatW835DOzt7dW5c+dM11uzZo2effZZXbx4UdLNOUBWrlypEiVK5FnbrImPj4/F61GjRt123hBJ5p3M0s0fbNM7ePBgjvZ9J8kGDw8PffPNNzp27Jh27Nih33//Xfv379eyZcuUnJysTZs2acKECWZS4G7cem4OHz6cb38H6XsHpaSk6IcfflDp0qW1efNmix/CX331Vb355pvy9fVVcnJyluewatWq2rdvn37++Wft3r1bBw8e1O7du7V69Wqlpqbq/fffV/PmzfXYY49ZHGepUqU0YMCALNsZFBSUB0f7f9L3cLGxscm0zq3vQ7du3cw5ezKTPjmSV+7kHN3Je3drL4K0z9o0p06dum37pJvzZaTNL5KZrOY5ycn/5K1turVdacdwa5tOnTplkbi89VjS5mNKP6dNeHi4FixYoNDQUNnb2+vZZ5/VkiVLbtvGvDy3AAAA+YVEBgAAKBQtWrSQt7e3eVfpK6+8YpY5OjpmObnqnUpISDCHSZKkp556yhzm5sCBA+bwO7lx67A5ycnJGerUrl1by5cvl3Rz+KUXXnhBHh4eFnUuX76sJUuWmImM48ePy87OTn5+fmrQoIE5fNFPP/2khx56SJJ09OhRnT171vyhy97e3hwqJbN23E5QUJAaNWqk7777TqmpqeratatZ9tRTT2V69+1HH32kvn37mue1TZs2mjdvXo5+2L9VZr0irNGtE177+vrqpZdeylBv37595t+2ZPmj4L///qtDhw6pfPnyunr1aqaTjeeVX375RaGhoQoMDNQzzzxjLu/Xr58++OADSbLo7ZPZ33Rmd6Jnpk6dOhav3377bS1btkz29v/3leOvv/5ScHBwro/jVrcmHdP+Bs+ePWuxvFOnTvL19ZUkLV68OMvtxcfHKyIiQuHh4RY9wapVq2Z+NuzevVuPPfaYateubW7r33//VePGjVW1alWL7RmGofXr1+eqV1JeCQ0NVfHixc1zcfnyZQ0cODBDvdOnT2vbtm15nmyRdEfn6E7euwceeEBubm7mEEgLFizQCy+8IFtbWxmGofnz52e63iOPPCI7Ozvz78bBwSHTc3TkyBEdOHAgw2d2bvz555/avn27+dmxfft2szeGJFWvXl1Sxs+Wzz77TOPHj5d08+87/YT0Pj4+5tBi6c/bY489pipVqki6ed7v5nP1Ts8tAABAfiGRAQAACoWTk5M6dOigjz76SJLlj+/NmzfP9RA6t1OyZEl5eXmZw2WMHj1ap0+fVkpKimbNmnVHw0mVLl3a4vXLL7+sJk2ayN7eXk8//bQqVaqkV199VStWrJBhGPrjjz/04IMPqnXr1vLz81NCQoJ+/vlnbdq0SZcuXTKTB5s3b1anTp1Up04dhYWFKSAgQDdu3NDSpUvNfTk6Olr8wFy6dGmzh8bEiRN19uxZOTs7KzIyUg0bNszR8XTv3t0c8zz9D22ZDSs1ceJEix/+SpcurUceeUQffvihRb2goCC1a9cuR/u/F1SrVk2PP/64OTxTnz59tHr1alWvXl22trb666+/tH37dv36668aMWKE+eN+zZo1LbYTFRWl+vXra/fu3frjjz/yrb0DBw7Uzp071bBhQwUFBalEiRI6fvy4Zs+ebdZJn2S59W+6Y8eOql27tmxtbdWlS5dsh5MJDw9X06ZN9c0330iSvvrqK1WrVk1NmzZVsWLFtG/fPm3evNli/oacWrNmjc6cOaMbN25o//79FsOe2dnZ6ZFHHpGUcd6Izp07q127djpy5IjmzZuX5fYfffRRBQQEqG7dugoICJCHh4f27NljkeBMO08xMTEaPXq0zpw5o5SUFEVFRalt27aqUKGCrl69qgMHDmjjxo06deqUNmzYkKdDbOWEra2tBgwYoCFDhki6mQT4888/9fjjj8vd3V0nT57Url27FBcXpzp16qhVq1a53sfIkSM1bdq0DMsDAgK0cuXKOzpHd/Le2dvbq2vXrmYc2bx5sxo0aKD69etr27ZtWr9+fabr+fj4qEePHpoxY4YkacKECdq1a5dq166tYsWK6Z9//tGOHTv0008/qVu3bmrSpEmuz1F6TZs2VY8ePWRjY6NZs2ZZtD+t92G1atXUsGFDs80TJkzQn3/+qSpVqui7776zmEOjX79+Zi+v0NBQ/fLLL5KkGTNmyNbWVi4uLpo3b95dDW12p+cWAAAg3xTePOMAAOB+t3PnTkNShseqVasyrd+tWzezTv369S3K0q8/e/bsTNcfN25cpvt78MEHjerVq5uvu3XrZq6zYcMGi7qHDx+22GZkZGSm21yyZIlZ58MPPzTs7e0zrZf+kWbBggW3rTtgwACLdrzyyiuZ1nv55Zdv/0b8f1euXDG8vb0t1vfz8zOuX7+eoW769yK7x63vkzUIDg6+bfvq169v1gkODrYoO3XqlBEREXHbYx8xYoTFenXr1s20XtOmTS1eb9iwwVxnxIgRWbYjq2NKv98mTZpk28ZixYoZO3fuNOtfuXLFKFWqVKZ1f/jhB8MwDGP27NmZ/t0ahmGcOXPGqFmzZpb78/T0zPIY0rv1/y67x8iRIy3WfeKJJzKtd+vfbPrPCScnp2z3UbZsWePChQtm/W3bthm+vr63bVv69/JOPr+yO9fZrXfjxg2jS5cuefb/mdP3I/3f6J2cozt5786dO2dUqlQp0/Wio6MtXqf//L506ZLRqFGj27YvN/EgTfr2VqxY0QgICMh02+PHj7dY78SJE0blypWzbU+bNm0sPpOzihelSpUyHn/88Uzf68OHD2f5HqR3p+cWAAAgP/zfYL0AAAAFrGbNmuYwGGn8/f1zPWFzTr3xxhv68MMPValSJTk4OMjf3189e/bUpk2b5ObmdkfbXLp0qVq1aiUfH58sx6zv3bu3fvrpJ73wwguqVKmSXFxcZG9vLz8/P9WvX1/Dhg3Tnj17zPp16tTRO++8o6eeekrly5eXu7u77O3tVaJECTVs2FBz5szRxIkTLfbxzjvvqF+/fgoMDJSdnd0dHUtaL5n0OnfubDE0EG727omLi9PHH3+sBg0ayNfXV3Z2dnJ1ddUDDzygzp07a/78+Xrttdcs1lu5cqWef/55lShRQk5OTqpatao+/fTTTO9szyuvvfaa+vXrp0cffVSlS5eWo6OjnJycVK5cOXXr1k07d+606C3i5OSkb775Ro0bN76j4XSKFy+ubdu26dNPP1WjRo1UokQJ2dvby9vbW9WrV1f//v3v+picnJwUHBysZ555RmvWrNHw4cMtyr/88kv1799fpUqVkqOjoypUqKAxY8Zo5syZWW7z448/Vvfu3VW1alWzzW5ubqpatapef/11xcXFWcyTULt2be3bt0/Dhg1T9erV5eHhITs7O3l5eal69erq06eP1q5dq3r16t318d4JW1tbzZ07V19//bXatGmjwMBA870PDg5W8+bNNXnyZC1YsCDf2nAn5+hO3jtvb29t3bpVPXv2NP+3qlWrptmzZ2vEiBFZrufi4qJvv/1Wn3/+uZo2bSo/Pz/Z29vL2dlZ5cuX1zPPPKNPPvnEnOT9TgUEBGjnzp3q1q2b2b6IiAjNnz9fr7/+ukVdf39//fDDD5o4caJq1aolT09P87P/iSee0MKFC/XFF19YfCa3b99eixcvVrVq1eTg4KDixYurXbt22rFjR7bzfuTEnZ5bAACA/GBjGIZR2I0AAAAAAAAAAADIDD0yAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBqkcgAAAAAAAAAAABWi0QGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBqkcgAAAAAAAAAAABWi0QGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBqkcgAAAAAAAAAAABWi0QGAAAAABSwOXPmyMbGRjY2NoqOji7s5gAAcN956623zFgcExOTq3Wjo6PNdefMmZMv7QNgyb6wGwAAd2POnDnq3r17tnXq16+vjRs3FkyDAAC4z124cEETJkzQqlWr9OeffyolJUXe3t7y9/dXeHi4GjdurC5duph1J0+ebK771ltvFUgbly9frvj4eEk3f4ggkQAAKCpSU1O1atUqzZ8/Xzt37tSpU6fk4OCg0qVLq3r16mrXrp2aNWsmGxubwm5qvpszZ46OHDkiSWrZsqUiIiJyvG76a5L+/fvLy8srT9sGIPdsDMMwCrsRAHCnSGQAAGA9zp8/r4cfflh//PFHlnWioqK0detWSdKRI0dUtmxZs6ygvprExMTos88+kySNGDGiwBIo6Z0+fVq///67JMnT01Ph4eEF3gYAQNFy6tQpPfvss9q8eXO29c6fP39f/DAfHR2tTZs2SZJmz56dodfF0aNHdfToUUmSn5+fKlasaJalT/QcPnxYISEhFuv+/PPPSkhIkCRVqlRJJUuWzIcjAJAePTIAFClbtmzJsMzT07MQWgIAwP1nypQpZhKjTJkyGjZsmMqVK6fLly9r3759WrlypWxt825020uXLsnV1TXPtleQSpYsyY8eAIA8k5ycrCZNmmjPnj2SJFtbW8XExKhZs2by9PTU33//rdWrV2vp0qWF3FLrUaZMGZUpU+aO1uUGBKDgMUcGgCKlTp06GR5pFxgbN240x7AMCQnR0aNH1aVLFxUvXlzOzs6qW7eudu3alWGbe/fu1RNPPCFXV1f5+PioQ4cOOnbsmEJCQsztpe/xsWDBArVo0UIVKlSQl5eXHBwcVLx4cdWvX1+zZs3K9G7TpUuXKjIyUsWKFVNQUJAGDRqk3377zdx+Zt1+d+7cqQ4dOigoKEiOjo7y9vZWo0aNtHLlyrw7oQAA5MLOnTvN56+++qqef/55NWjQQE899ZRef/11bd26VV9//bWkm3dJpu+NIcki7qXF1vTx9rvvvtOIESNUrlw52dvba8aMGZJuDv/QsGFDlSlTRm5ubnJ0dFRAQIBatWplEaPTrgXSemNI0siRIzOdq+LGjRv6z3/+o7p168rb21uOjo4KDg5Wz549dfjw4QzHnpycrIEDByogIEDOzs6qWbOmVq1aleX427ebI2PFihVq1qyZ/P395ejoKF9fX0VFRVm0HQCANFOmTDGTGJI0f/58zZw5U61atVKDBg3UrVs3LVy4UL/88otcXFwk3ewJOWfOHD322GPy8fGRg4OD/P391aJFC61fvz7DPtLH6b179+qll15SiRIl5O7urubNm+vIkSMyDENTpkxRxYoV5eTkpLCwMM2fP99iO5l9N+/YsaOKFy8uFxcX1atXT9u2bcuw/5zG5rQYm9YbQ5K6d++eIR5nFqNjYmIyfP8uW7ZshvkwMpsjo0OHDuaysWPHWmwjOTlZ7u7uZnlar0xJ+vXXX/X888+rXLlyKlasmDw8PBQVFaU5c+YUWG9V4J5gAMA9bPbs2YYk85GdDRs2mPU8PDyMkiVLWqwryfD19TUSExPNdX7++WfDw8MjQ73g4GDDx8fHfL1hwwZznXbt2mWon/7Rr18/i3Z99tlnmdZ76KGHsjy2Dz/80LC1tc1yH4MGDbrrcwsAQG61b9/ejEWhoaHGwoULjZMnT2Zat379+tnGy7TYGhwcbC6rWLGiRZ3333/fMAzD8PPzy3I7NjY2xpdffmkYhuW1QGaP+vXrG4ZhGMnJycZjjz2WZT0vLy8jLi7OPJYbN24YjRo1ynTfERER5utu3bqZ66S/hknbr2EYRmpqqhETE5Plvlu0aJFXbxcAoAgJDQ01Y0WDBg1uWz8lJcVo2bJltnHxnXfesVgnfVmlSpUy1C9btqzRq1evTLe1fft2czvp47GXl5cREBCQob6jo6OxceNGc53cxOZbfye49ZEWj0eMGJFhWbdu3bJdd/bs2YZhWF7HpC1bu3atuezBBx+0OHcLFiwwy+rWrWsuX7ZsmVGsWLEs99epUycjNTU1R38DQFFHjwwARUr6O0TSHuknEU2TmJgoV1dXff7555o9e7Y5/NSZM2f0+eefm/X69eunxMRESVKpUqX02Wef6YsvvpCrq6vOnTuXaRuefvppTZ8+XStXrtSGDRu0fv16zZw5U76+vpKkadOm6eTJk5KkpKQk9e3b11y3du3aWr58uT7++GMdOnQo0+3v27dPsbGxSk1Nla2trYYMGaLvvvtO//nPf+Tt7S1JGjt2rP73v//l8uwBAHB3nnrqKfP5gQMH1L59e/n7+yswMFAdOnTQypUrzTsLp06dqiVLllisv2XLFvMRGRmZYfsHDx5U9+7d9dVXX2nx4sWqXr26pJuTcH722Wf6+uuvtXHjRn377bcaM2aMJMkwDA0bNkySFBkZqS1btujJJ580t9m9e3dzn1OnTpV08w7NDRs2SLp5F+bs2bP13XffqVevXpJuTlLeoUMHpaSkSLp51+u6desk3bwWee211/TNN9/o+eefNycVz6kZM2aYd3ZK0jPPPKMvvvhCK1eu1NChQ1WiRIlcbQ8AUPRdunRJBw4cMF83btz4tut8+OGHWr58uSTJwcFBo0aNMmNXmiFDhlj0tkzv33//1cyZMzV//nxzmMfDhw9r+vTp6tOnj7755hvVrl3brP/BBx9kup0LFy7I09NTixcv1qJFi1SpUiVJ0rVr1/TCCy+Y1w25ic1NmzbVli1bLCb3Hjx4sBnvhwwZkuV5GTJkSIYhq5csWWKu27Rp0yzXbdiwoTmXxi+//KK9e/eaZel/Z3juuefMc9ilSxdduXJFktSrVy+tWbNG8+bNU3BwsKSb1xizZ8/Ocp/AfaWQEykAcFdud6eF0t2teetdmDt37jS3k/6ukQEDBhiGYRj//vuvRf2lS5ea9X/55ZdM7xo1DMM4c+aM8cYbbxjh4eGGq6urYWNjk6FNK1euNAzDML744guLO07S37U6bdo0i3XSvPrqq+ayRo0aGVu2bDEfPXr0MMvat2+fH6ccAIBsvfzyy5nGvrRHixYtzDsLDx8+nGmsSy99j4zWrVtnWmffvn1G165djbJlyxpOTk6Z7jd9j8v0d1uOGDHCYlupqalGiRIlzPJJkyZZxNpSpUqZZWvWrDEMwzCaNWtmLnv66acttlejRo0Md3saRtY9MtLXb9WqVU5POwDgPnbs2DGLmDdjxozbrhMZGWnWj42NtShLH4t69+5tLk+/j48++shc3rRpU3P5ww8/bC5fsmSJufyhhx4yl9/63fyXX34xy3bt2mVRtnv37juKzYaRea+J9DLrkZHZsR4+fDjDullte+TIkeby119/3TAMwzh79qzh4OBgSDdHh7h06ZJhGIYxdepUix4c6Y9pyJAhZtmjjz6ayTsI3H+Y7BtAkZLZZN/lypXLsMzd3V01a9Y0XxcvXtx8ntbTIm2y0jRRUVHm8ypVqsjLy0sXLlywqHP58mVFRUVZ3A2TmfPnz0u6eWdpmvLly8vPz898XadOnUzX3b9/v/l83bp15h2gt/rll1+ybQMAAPlh2rRpio2NNe9ejIuLU0JCglm+YsUKLVq0SO3bt8/1ttu0aZNh2c8//6xatWrp0qVL2a57/vx5ubu733Yf//77r/7991/z9YABA7Ks+8svv6hJkyYW8Tz99YJ0M55nNgdXVtLH+datW+d4PQDA/cvLy8vi9dmzZ2+7zm+//WY+v/W7Z/rYlb5eeul7W6T/Pl2rVi3zedqoBJKyHNHA29tbVapUMV9Xr15dzs7Ounz5sqSb35lLly6d69hcWGJiYjRy5EilpqZqwYIFGjdunJYsWaLr169Lktq3b2/OUZI+5v/yyy+qW7duptvkuz1wE0NLAShSMpvsOyAgIEM9Hx8fi9f29v+X1zX+f9fVWyf4ymzC7VstW7bMTGK4urrqgw8+0IYNG7RlyxZz0nFJSk1NzbDNnGw/N5KSkvJ0ewAA5FRoaKiGDh2qb7/9VmfPntWaNWvM4Q8lKS4u7o62W6pUqQzLpk6daiYxKlasqPnz52vz5s0Wk3xL/xd781JarM3PeA4AwO24uroqNDTUfJ3VzW55KW14Zkmytf2/nxdvTaqkMQpo0urC/h5cpkwZNWrUSJL0999/a/PmzZkOK5UbhX1MgLUgkQEAWahQoYLFjxE7duwwn//yyy8ZemNI0tGjR83nTzzxhGJjYxUdHa2qVavq2LFjGepXrFjRfH7o0CGdOXPGfJ1Z7xJJCgsLM5936NBBhmFk+uCuDQBAQduwYUOG+GhnZ6cmTZrokUceMZelJRXS//CRfnlWMksSpI+9ffv2VceOHVW3bl3Z2dlluZ30+711nyVKlLC4g/Tbb7/NNM4mJSVpxIgRkizjefrrBUnaunVrtsd0q8qVK5vPly1blqG8oH4IAgDcW2JiYszn69atyzAPVZqDBw/q2rVreuCBB8xl27Zts6iT/nX6evnh/Pnz+vXXX83Xu3fvNntjSDe/l99JbJayj/e3k/6aI7frpk9WjBs3zvxuHx4erocfftgsS//dvnbt2ll+tyeRAdzE0FIAipTMfiywt7fXo48+muttFS9eXA0aNND69eslSS+//LISEhLk7OxscXGUXvphrNavX6958+bJ09NT7733njmcVHqNGzeWp6enEhISdPXqVbVp00avvvqqTpw4oaFDh2a6j5iYGE2ePNnsquru7q5mzZrJyclJx44d0/79+7Vy5UoNHjzY4mIWAID8NnPmTC1dulTNmjXTY489pvLly8vGxkZbt27V2rVrzXppw074+PjIxsbG/HH+/fff18MPPyxbW9sMQzRlJX3s/fTTTxUSEqJz585lGUclyyEwvvnmG9WpU0cuLi4KDg5WUFCQunfvrnfffVeS1LVrV7355pt68MEHlZSUpKNHj2rHjh366quvlJiYKEl69tlntWrVKknS8uXLNWTIENWpU0fLli3L1bBSkvT888+b6yxdulTt27dXu3bt5ODgoB9//FHHjh3TjBkzcrVNAEDR169fPy1cuFB79uyRdPOmt++++07NmjWTh4eH/vnnH61Zs0ZLlizRqVOnFBMTo59++kmSNH36dJUsWVLVq1fXsmXL9MMPP5jb7datW763vW3btuZ37OHDh5vLK1asqMjISNnY2OQ6NkuW8X7JkiUKCQmRo6OjQkNDVaJEiWzbVLx4cfNGw+nTp6tZs2aytbXVww8/LEdHx2zXbdmypYoXL272Sk1za2+Mdu3aafDgwUpKStL27dv1zDPPqGPHjvL09NQ///yjAwcO6JtvvlHLli2z/A0CuK8U1GQcAJAfcjLZt6enp2EYlhOKBQcHW2wnq0m+fvnlF8Pd3T3DNoOCggwfH58Mk31funTJKFeuXIb6/v7+xgMPPJDpZGCfffZZpu2OiIjIcgLUadOmGba2ttked2aTmQEAkJ86dep027hcr149IyUlxVynVq1aGerY2dmZ5ekn+06Lt+nt3bvXnEAz/SM6OjrLiTq//fbbTNv29ttvG4ZhGMnJyRnWz+yR5saNG0ajRo0ylNvY2BhVq1bN9Bojq8m+b9y4YXTp0iXLfbZo0eKu3ycAQNF04sQJo169ereNX+fPnzdSUlKMli1bZltv9OjRFtvPKq5269bNXD5ixAhzeVbfwdMv9/HxsYj1aQ8HBwdj/fr15jq5jc2GYRj/+c9/Mq0zb948wzCyn+y7Q4cOma77999/G4Zx+4nE+/XrZ7Geo6OjcebMmQz1li5dahQrVizbY0p/ToH7GUNLAUA2qlSpoq1bt6px48ZycXGRp6en2rZtq23btll0L3V1dZUkubi46H//+59atWolHx8feXp66umnn9bWrVstJvJOr2vXrvriiy9UrVo1OTo6KiAgQAMHDtRHH31k1kmbDCzNyy+/rB07dqhTp04qU6aMHB0d5eHhodDQULVt21Zz585lglAAQIF76623NGXKFLVs2VJhYWHy8fGRnZ2dvLy8VKtWLU2cOFHfffedxbBP8+bNU9OmTXM0EXdmwsPDtXbtWtWqVUuurq7y9/dXnz59zB4SmWncuLEmTZqk8uXLZzoElbOzs9atW6dPPvlE0dHR8vHxkb29vfz8/FS9enW98sorFnNw2NraasWKFRowYID8/f3l5OSkhx56SEuXLlWDBg3MemnXC9mxtbXV3Llz9cUXX6hp06YqWbKk7O3t5ePjo9q1a6tly5a5Oj8AgPuHv7+/NmzYoGXLlumZZ55RmTJlVKxYMbm5uSk0NFQdO3bUihUr5OnpKTs7Oy1dulSzZs1S/fr15eXlJXt7e5UsWVJPP/201q1bpyFDhuR7m93d3bV9+3Z17txZPj4+KlasmOrUqaN169ZZxNDcxmbpZg+IQYMGKTAwMMNwlrczZcoUtWvXzuw9mlu39r5I66Vxq1atWumnn37SCy+8oAoVKqhYsWJydXVVhQoV1KxZM02fPl29e/fO9f6BosjGMBhkFQBy6+eff1bVqlUl3fzB4cyZMxaTmOaGYRiZXhhNnTpVffv2lSRFRESY3X4BAID1ySyeG4ahmjVr6scff5QkTZ48Wf369SuM5gEAYDU2btyoxx57TJIUHBysI0eOFG6DANwTmCMDALJx5coVRUdHq0+fPoqIiJC7u7v27NmjN954w6zTrFmzO05iSNLatWs1a9Ysde7cWWFhYUpJSdGGDRs0bNgws07Xrl3v6jgAAED+io2NlZ+fnxo2bKgyZcro5MmTmjp1qpnEcHZ2Vtu2bQu5lQAAAMC9iUQGANxGXFyc4uLiMi2rWLGiPv7447vafmpqqhYtWqRFixZlWt6iRQvFxsbe1T4AAED+OnPmjD788EOLSUrTODg4aMaMGQoICCiElgEAAAD3PhIZAJANBwcHxcbGasuWLTp69KgSExPl5uamsLAwtWzZUi+//HKOxrvOTqVKldSlSxfFxcXpxIkTunLlinx8fBQZGamuXbuqffv2dzQmJwAAKDitWrXSuXPntG/fPp05c0Z2dnYKCgpS/fr11bdvXz344IOF3UQAAADgnsUcGQAAAAAAAAAAwGrZFnYDAAAAAAAAAAAAskIiAwAAAAAAAAAAWC3myCjiUlNTdfz4cbm7uzPGPgCgyDAMQxcvXlRAQIBsbYvWfRnEbgBAUVVU4zexGwBQVFlT7CaRUcQdP35cQUFBhd0MAADyxd9//63AwMDCbkaeInYDAIq6oha/id0AgKLOGmI3iYwizt3dXdLNPzYPD49Cbg0AAHkjMTFRQUFBZpwrSojdAICiqqjGb2I3AKCosqbYTSKjiEvr1urh4cEFFQCgyCmKwzcQuwEARV1Ri9/EbgBAUWcNsbvoDEoJAAAAAAAAAACKHBIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC17Au7ASgYz37VUQ4uDoXdDADAfWpVy2WF3YR7DrEbAFDYiN+5Q+wGABS2ohy76ZEBAAAAAAAAAACsFokMAAAAAAAAAABgtUhkAAAAAAAAAAAAq0UiAwAAAAAAAAAAWC0SGQAAAAAAAAAAwGqRyAAAAAAAAAAAAFaLRAYAAAAAAAAAALBaJDIAAAAAAAAAAIDVIpEBAAAAAAAAAACsFokMAAAAAAAAAABgtUhkAAAAAAAAAAAAq0UiAwAAAAAAAAAAWC0SGQAAAAAAAAAAwGqRyAAAAAAAAAAAAFaLRAYAAAAAAAAAALBaJDIAAAAAAAAAAIDVIpEBAAAAAAAAAACsFokMAAAAAAAAAABgtUhkAAAAAAAAAAAAq0UiAwAAAAAAAAAAWC0SGQAAAAAAAAAAwGqRyAAAAAAAAAAAAFaLRAYAAAAAAAAAALBaJDIAAEChuH79uvr06SNvb2/5+PgoNjZWKSkpmdZ1c3OzeBQvXtyiPDY2VkFBQfLw8FDp0qXVv39/Xbt2zSyPjo6Wk5OTxTaOHz8uSTp9+rQ6deqkwMBAeXh4KDIyUitXrsy/AwcA4B6Vm9h96NAhPfnkk/L29lbp0qU1YcIEi/LExER17NhRHh4e8vPz09tvv53pdk6dOiUfHx9FRESYy65evaro6GiVLFlSHh4eeuCBB/TJJ5/k2XECAFBUFHTs/vTTTxUaGipXV1eFhIRoxYoVZplhGBo7dqxCQkLk6uqqSpUqKS4uLsfHQiLjHhEdHa3+/fsXdjMAAMgzo0eP1tatW7V//37t27dPW7Zs0ZgxYzKtm5SUZPEIDQ21KO/du7d+++03JSYmas+ePdqzZ0+Gi67x48dbbCMgIMDcdmRkpHbs2KELFy5o1KhR6tChg/bv358/Bw4AwD0qp7H7xo0bevrpp/XQQw/p9OnT+t///qdp06ZpyZIlZp3Y2FidO3dOR48e1ZYtWzRjxgzNnTs3w7b69OmjyMhIi2X29vaaOnWqjh8/rsTERC1dulTDhg3Tli1b8v6gAQC4hxVk7P7kk080ceJELVy4UElJSYqLi1N4eLhZPmTIEH399ddat26dkpKStHbtWpUpUybHx0Iiw8qlv5sUAICiZNasWRo6dKhKlSqlUqVKaciQIZo5c+Zt19u5c6d+++03i2VhYWFydXWVdPMuD1tbWx08eDBH7ShXrpwGDhyowMBA2draqnnz5goNDdWOHTtyf1AAABRhOY3dBw4c0IEDBzRixAg5ODgoNDRUzz33nObMmSNJSk5O1sKFCzV69Gh5eXmpUqVKio2NzbCtFStW6Ny5c+rSpYvFcjs7O4WHh8ve3l6SZGNjIxsbG/3xxx/5c+AAANyjCip237hxQ8OHD9eUKVMUGRkpGxsb+fn5qVy5cpKkc+fOadKkSZo1a5YqVKggGxsbBQcHq1SpUjk+FhIZt/HFF18oPDxczs7OKl68uBo1aqRLly4pJiZGLVu21JgxY+Tn5ycvLy+NGjVKKSkpeu211+Tj46PAwEDNnj3bYntvvPGGKlWqJBcXF5UrV07Dhg3T9evXzfK33npLERER+vTTT1W2bFkVK1ZMMTEx2rRpk6ZMmWJeoB05cqSAzwQAAHnn/PnzOnbsmMUwERERETp69KgSEhKyXXfmzJl6/PHHMywfN26c3NzcVLJkSe3Zs0exsbEW5aNHj5aPj48iIyMzveMzzenTp/Xrr7+qatWquTsoAACKsNzE7tTUVEk3by5Iv+yXX36RJB08eFDXrl3LsK29e/earxMSEjRgwABNnz49yzY1a9ZMxYoVU+XKleXn56dWrVrdzSECAFCkFGTsPnDggE6dOqXdu3crJCREgYGB6tmzpxITEyVJO3bskJOTkxYsWKCAgACFhITojTfeyNVN/CQysnHixAl16NBBPXr00K+//qqNGzeqdevW5hv6v//9T8ePH9fmzZs1adIkjRgxQs2aNZO3t7fi4uLUq1cvvfjiizp27Ji5TXd3d82ZM0f79+/XlClTNGPGDL3//vsW+/3jjz/05ZdfaunSpYqPj9eUKVNUq1Yt9ezZUydOnNCJEycUFBRUoOcCAIC8lJSUJEny8vIyl6U9v3jxYpbrXbp0SQsXLlTXrl0zlL355ptKSkrS/v371atXL/n7+5tlY8eO1aFDh3Tq1CmNGzdOsbGxWrZsWYZtXLt2Te3bt9ezzz6rGjVq3OHRAQBQ9OQmdoeGhiokJETDhw/X1atXtW/fPs2aNcusd+nSJbm6upo9KtK2lX47r7/+umJiYlSxYsUs2/TVV1/p0qVL2rhxo9q0aSNnZ+e7PUwAAIqMgozd586dkyStW7dOu3btUnx8vA4fPqxXXnnFLE9MTNTBgwf1+++/a/PmzVq9erXGjx+f4+MhkZGNEydOKCUlRa1bt1ZISIjCw8PVu3dvubm5SZJ8fHz0wQcfKDQ0VD169FBoaKiSk5M1ePBgVaxYUYMGDZKjo6O2bt1qbnPo0KGqXbu2QkJC1Lx5cw0cOFCLFy+22O+1a9c0d+5cRUZGqmrVqvL09JSjo6NcXFzk7+8vf39/2dnZZdrmq1evKjEx0eIBAIC1SYul6e8CSXvu7u6e5XpLliyRi4uLmjRpkmWdsLAwVatWTTExMeayWrVqydPTUw4ODmrSpIlefPFFLVq0yGK9a9eu6ZlnnpGLi4tmzJhxJ4d1R4jdAIB7QW5it4ODg1asWKGffvpJpUuXVqdOndS9e3f5+PhIklxdXZWcnGwx2WhCQoK5nS1btmjbtm164403btsuOzs71a9fX6dOndK77757dweZQ8RuAMC9oCBjd9q+Bg0aJF9fX/n6+mrQoEFatWqVRfnIkSPl5uamMmXKqF+/fmZ5TpDIyEa1atXUsGFDhYeHq23btpoxY4bOnz9vllepUkW2tv93Cv38/CwmMLGzs1Px4sV1+vRpc9miRYsUFRUlf39/ubm5aejQoTp69KjFfoODg1WiRIk7avPYsWPl6elpPui5AQCwRt7e3goMDFR8fLy5LD4+XkFBQfL09MxyvU8//VTdunWzuAskM9evX892joz08Vu6mcRo27atrl27pi+//FKOjo45O5A8QOwGANwLchu7q1Spou+++05nzpxRfHy8rl69qqioKElSxYoV5eDgoD179lhsK+379Pr16/Xnn38qICBAvr6+io2N1S+//CJfX1+dOHEi0/bdLvbnJWI3AOBeUJCxOzQ0VMWKFcuyLdWqVbvr4yGRkQ07OzutXbtWq1evVuXKlTV16lSFhobq8OHDkm5mqtKzsbHJdFnaGGPff/+9OnXqpKZNm+qrr77STz/9pCFDhmQYCyxtstI7MWjQICUkJJiPv//++463BQBAfurevbveeecdnTx5UidPntSYMWP0/PPPZ1n/wIED2r59u5577jmL5UlJSZo9e7YuXLggwzD0888/a/To0WavjQsXLuibb75RcnKybty4ofXr12v69Olq06aNpJs/fDz77LO6dOmSli9fLicnp/w76EwQuwEA94rcxO69e/fq0qVLunbtmpYuXapZs2bptddekyS5uLioXbt2GjZsmBISEnTw4EFNnTrV3NaAAQP0+++/Kz4+XvHx8Ro1apRCQ0MVHx+vkiVLKj4+XmvXrtXly5eVkpKir7/+WvPnz8+2x2ZeInYDAO4VBRW7nZ2d1blzZ40fP17nz5/XhQsXNH78eLVo0UKSVLZsWTVq1EijRo1ScnKyjh8/rqlTp5rlOZH97YyQjY2NoqKiFBUVpeHDhys4ODjTMbVzYvv27QoODtaQIUPMZX/99VeO1nV0dNSNGzduW8/JyanAf4ABAOBODBs2TGfPnlVYWJgkqXPnzho8eLAkqVevXpJkMcHnzJkzVbduXVWsWNFiCAcbGxt9/vnnGjhwoK5evaqSJUuqTZs2GjlypKSbiYqRI0eqffv2kqSQkBBNmjRJbdu2lXQzPq9YsULFihWTr6+vud3Bgweb7clPxG4AwL0iN7F78eLF+vjjj3XlyhVVq1ZNy5cv14MPPmhua9q0aXrxxRcVGBgoZ2dn9enTx5wDy8PDQx4eHmZdb29vOTg4KDAwUJKUkpKiwYMH68CBA7KxsTFje8eOHfP/JIjYDQC4dxRU7JakyZMn6+WXX1bZsmXl5OSkp59+WpMmTTLL58+frxdeeEF+fn7y8PBQ586d9frrr+f4WGyM9FORw0JcXJzWr1+vxo0bq2TJkoqLi1Pnzp21fPlyLVq0SBcuXNDy5cvN+tHR0YqIiNDkyZPNZSEhIerfv7/69++vlStXqk2bNpo3b55q1qypr7/+WiNHjtSNGzd04cIFSdJbb72l5cuXW3T5kaQXXnhB8fHxWrx4sdzc3OTj45NhWIzMJCYmytPTU03mPyUHF4fb1gcAID+sanlnNwFkJS2+JSQkWPzQURQQuwEA1oL4nTPEbgCAtSjKsZuhpbLh4eGhzZs3q2nTpqpUqZKGDh2qiRMn6sknn7yj7T399NN65ZVX1KdPH0VERGj79u0aNmxYjtYdOHCg7OzsVLlyZZUoUSLDvBoAAAAAAAAAABRF9Mgo4rgzBABgDYryXSF5jdgNALAWxO+cIXYDAKxFUY7d9MgAAAAAAAAAAABWi0QGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBqkcgAAAAAAAAAAABWi0QGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBqkcgAAAAAAAAAAABWi0QGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgt+8JuAArG4mafy8PDo7CbAQAAcojYDQDAvYXYDQBA/qFHBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLfvCbgAKRvPFybJ34e0GsrO+o0thNwEATMRuANaKayYgc8RuWAM+owEUVfTIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZt9i4caNsbGx04cKFHK8TExOjli1b5lubCno/AP7P9evX1adPH3l7e8vHx0exsbFKSUnJtG5sbKyCgoLk4eGh0qVLq3///rp27ZpZHh0dLScnJ7m5uZmP48ePW2zj008/VWhoqFxdXRUSEqIVK1aYZcePH1fTpk3l6uqqMmXKaMaMGflz0AAAALmUm2smSVq5cqUiIiLk6uqqgIAATZ8+PUOdU6dOycfHRxERERbLX3jhBYWGhsrW1laTJ0+2KLt27ZqeeeYZhYSEyMbGRsuXL8+DowOAe1dBfqdNTExUx44d5eHhIT8/P7399tsW2//xxx9Vp04deXh4qFy5cpo7d27+HDSAIolExi1q166tEydOyNPTM8frTJkyRXPmzMm/RgEoNKNHj9bWrVu1f/9+7du3T1u2bNGYMWMyrdu7d2/99ttvSkxM1J49e7Rnzx5NmDDBos748eOVlJRkPgICAsyyTz75RBMnTtTChQuVlJSkuLg4hYeHm+UdOnSQv7+/Tp8+rSVLlui1117Tpk2b8ufAAQAAciE310xr1qxR7969NXnyZCUmJmrfvn2Kjo7OUK9Pnz6KjIzMsLxatWr66KOP9PDDD2e6/Tp16mjevHkKDAy8q2MCgKKgIL/TxsbG6ty5czp69Ki2bNmiGTNmmMmKCxcuqGnTpurcubPOnz+vBQsWKDY2Vlu3bs2/gwdQpJDIuIWjo6P8/f1lY2OT43U8PT3l5eWVf40CUGhmzZqloUOHqlSpUipVqpSGDBmimTNnZlo3LCxMrq6ukiTDMGRra6uDBw/maD83btzQ8OHDNWXKFEVGRsrGxkZ+fn4qV66cJOnQoUPaunWrxo4dK1dXVz3yyCPq1KmTZs2alTcHCgAAcBdyc800bNgwDR8+XNHR0bKzs5O3t7ceeOABizorVqzQuXPn1KVLlwzrv/zyy2rYsKGKFSuWoczR0VH9+/dX3bp1ZWdnlzcHBwD3sIL6TpucnKyFCxdq9OjR8vLyUqVKlRQbG2vua/v27XJyclKvXr1kZ2enRx55RK1bt9ann36aNwcKoMgr8omM6OhoxcbGqn///vL29pafn59mzJihS5cuqXv37nJ3d1eFChW0evVqSRmHlpozZ468vLz07bffKiwsTG5ubnriiSd04sQJcx+3DvmU231KN3/EfO6551S2bFk5OzsrNDRUU6ZMKZBzBCBz58+f17FjxyyGM4iIiNDRo0eVkJCQ6Trjxo2Tm5ubSpYsqT179ig2NtaifPTo0fLx8VFkZKRFN9oDBw7o1KlT2r17t0JCQhQYGKiePXsqMTFRkrR3716VKlVKfn5+Fm3Zu3dvHh4xAABA7uXmmunSpUv68ccf9c8//6hSpUry9/dX27ZtLb5fJSQkaMCAAZkONwUAyLmC/k577dq1DPtK+86ampoqwzAstpWamsp3WgA5VuQTGZL02WefydfXVzt37lRsbKxeeukltW3bVrVr19bu3bvVuHFjdenSRcnJyZmun5ycrPfee0/z5s3T5s2bdfToUQ0cODBP95mamqrAwEAtWbJE+/fv1/DhwzV48GAtXrw4V8d69epVJSYmWjwA3JmkpCRJsuhxlfb84sWLma7z5ptvKikpSfv371evXr3k7+9vlo0dO1aHDh3SqVOnNG7cOMXGxmrZsmWSpHPnzkmS1q1bp127dik+Pl6HDx/WK6+8Yrbl1p5fXl5eWbYDwL2D2A3gXpeba6bz58/LMAwtX75ca9eu1R9//CEnJyd17tzZrPP6668rJiZGFStWzPe2A3eC2I17RUF+p01KSpKrq6vs7e0t9pW2n1q1aunSpUuaNm2arl+/rm3btmnZsmX8/wDIsfsikVGtWjUNHTpUFStW1KBBg1SsWDH5+vqqZ8+eqlixooYPH66zZ89mmQW+fv26pk+frho1auihhx5Snz59tH79+jzdp4ODg0aOHKkaNWqobNmy6tSpk7p3757rRMbYsWPl6elpPoKCgnK1PoD/4+bmJkkWd6qkPXd3d8923bCwMFWrVk0xMTHmslq1asnT01MODg5q0qSJXnzxRS1atMhiX4MGDZKvr698fX01aNAgrVq1yiy/9Y6ZhISE27YDgPUjdgO41+Xmmimtbt++fRUcHCw3NzeNHDlSGzZs0KVLl7RlyxZt27ZNb7zxRgG1Hsg9YjfuFQX9nTY5OdliIvH031mLFy+uVatW6fPPP5e/v7/efPNNde/eXcWLF8+TYwVQ9N0XiYyqVauaz+3s7FS8eHGLCXTThmo5ffp0puu7uLiofPny5utSpUplWfdu9vnhhx+qevXqKlGihNzc3PTJJ5/o6NGjOTlE06BBg5SQkGA+/v7771ytD+D/eHt7KzAwUPHx8eay+Ph4BQUFydPT87brX79+PdvxRG1t/+8jODQ0NNNxntNUrVpVx48ft/jMiI+Pt/hcAXBvInYDuNfl5prJy8tLZcqUyXQ7hmFo/fr1+vPPPxUQECBfX1/Fxsbql19+ka+vr8XwU0BhInbjXlHQ32kdHBy0Z88ei32l/84aFRWl7du36+zZs9qyZYtOnjyp+vXr5/KoANyv7otEhoODg8VrGxsbi2VpE3unpqbmeP1bx/W7230uXLhQAwcO1HPPPafvvvtO8fHx6t69u65du5btfm7l5OQkDw8PiweAO9e9e3e98847OnnypE6ePKkxY8bo+eefz1AvKSlJs2fP1oULF2QYhn7++WeNHj1aTZo0kSRduHBB33zzjZKTk3Xjxg2tX79e06dPV5s2bSRJzs7O6ty5s8aPH6/z58/rwoULGj9+vFq0aCFJKl++vKKiojR48GAlJydr586dmj9/vp577rmCOxkA8gWxG0BRkNNrJkl64YUXNHXqVP3zzz+6fPmyRo0apYYNG8rNzU0DBgzQ77//rvj4eMXHx2vUqFEKDQ1VfHy8SpYsKUm6du2arly5otTUVKWkpOjKlSsWdwBfvXpVV65ckWEYun79uq5cuaIbN24UyHnA/YHYjXtJQX2ndXFxUbt27TRs2DAlJCTo4MGDmjp1qsW+fvrpJ129elWXL1/WjBkztHHjRvXv379AzgOAe999kci4F2zbtk21a9dW7969FRkZqQoVKujQoUOF3Szgvjds2DDVqlVLYWFhCgsLM5MJktSrVy/16tVL0s3k5Oeff67y5cvL3d1dLVq00FNPPaXJkydLunkny8iRI+Xv7y9vb2+98sormjRpktq2bWvua/LkyQoICFDZsmUVGhqq4OBgTZo0ySxfsGCB/vnnH5UoUUJt2rTRhAkTuHsFAABYhZxeM0k3x19v2LChqlWrpqCgICUnJ2vevHmSJA8PDwUGBpoPb29vOTg4KDAwUHZ2dpKkxo0by9nZWVu2bNFrr70mZ2dnjR492tx+aGionJ2ddfToUT377LNydnY2tw8A95uC/E47bdo0eXp6KjAwUFFRUXruuefUtWtXs/yDDz6Qn5+fSpQooSVLluh///ufAgICCu5kALin2d++CgpCxYoVNXfuXH377bcqW7as5s2bpx9++EFly5Yt7KYB9zUHBwd9+OGH+vDDDzOUTZ8+3Xzu6uqqtWvXZrmdEiVKKC4uLtt9ubq6as6cOVmWly5dWqtXr759owEAAApYTq+ZpJtD706cOFETJ0687XZjYmIsxmeXpI0bN2a7zpEjR267XQC4XxTkd1oPDw8tWLAgy/LZs2dr9uzZOWg1AGREjwwr8eKLL6p169Zq166dHnnkEZ09e1a9e/cu7GYBAAAAAAAAAFCobIzbTfaAe1piYqI8PT1Vb8YJ2bswbieQnfUdXQq7CQByKC2+JSQkFLlxqYndAKwd10y4U0U1fhO7YU34jAaQl6wpdtMjAwAAAAAAAAAAWC0SGQAAAAAAAAAAwGqRyAAAAAAAAAAAAFaLRAYAAAAAAAAAALBaJDIAAAAAAAAAAIDVIpEBAAAAAAAAAACsFokMAAAAAAAAAABgtUhkAAAAAAAAAAAAq0UiAwAAAAAAAAAAWC0SGQAAAAAAAAAAwGqRyAAAAAAAAAAAAFaLRAYAAAAAAAAAALBaJDIAAAAAAAAAAIDVIpEBAAAAAAAAAACsFokMAAAAAAAAAABgtUhkAAAAAAAAAAAAq0UiAwAAAAAAAAAAWC0SGQAAAAAAAAAAwGqRyAAAAAAAAAAAAFaLRAYAAAAAAAAAALBaJDIAAAAAAAAAAIDVIpEBAAAAAAAAAACsFokMAAAAAAAAAABgtewLuwEoGKuedZGHh0thNwMAAOQQsRsAgHsLsRsAgPxDjwwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFr2hd0AFIz//ve/cnZ2LuxmAIC6d+9e2E0A7gnEbgAFifgM3D1iN4B7GdcCsHb0yAAAAAAAAAAAAFaLRAYAAAAAAAAAALBaJDIAAAAAAAAAAIDVIpEBAAAAAAAAAACsFokMAAAAAAAAAABgtUhkAAAAAAAAAAAAq0UiAwAAAAAAAAAAWC0SGQAAAAAAAAAAwGqRyAAAAAAAAAAAAFaLRAYAAAAAAAAAALBauU5kNGjQQBcuXMiwPDExUQ0aNMiLNgEAAAAAAAAAAEi6g0TGxo0bde3atQzLr1y5oi1btuRJowAAAAAAAAAAACTJPqcV9+7daz7fv3+/Tp48ab6+ceOG1qxZo9KlS+dt6wAAAAAAAAAAwH0tx4mMiIgI2djYyMbGJtMhpJydnTV16tQ8bRwAAAAAAAAAALi/5TiRcfjwYRmGoXLlymnnzp0qUaKEWebo6KiSJUvKzs4uXxoJAAAAAAAAAADuTzlOZAQHB0uSUlNT860xAAAAAAAAAAAA6eU4kZHewYMHtWHDBp0+fTpDYmP48OF50jAAAAAAAAAAAIBcJzJmzJihl156Sb6+vvL395eNjY1ZZmNjQyIDAAAAAAAAAADkmVwnMkaPHq133nlHb7zxRn60BwAAAAAAAAAAwGSb2xXOnz+vtm3b5kdbAAAAAAAAAAAALOQ6kdG2bVt99913+dEWAAAAAAAAAAAAC7keWqpChQoaNmyYduzYofDwcDk4OFiU9+3bN88aBwAAAAAAAAAA7m+5TmR88skncnNz06ZNm7Rp0yaLMhsbGxIZAAAAAAAAAAAgz+R6aKnDhw9n+fjzzz/zo433nJiYGLVs2bKwmwEA94Tr16+rT58+8vb2lo+Pj2JjY5WSkpJp3ZiYGDk6OsrNzc18fP/992b5tGnTVKNGDTk5OWX6OfzMM8+oVKlS8vDwUNmyZTV69GiL8hdeeEGhoaGytbXV5MmT8/IwAQC4p+QmPkvSypUrFRERIVdXVwUEBGj69Olm2bBhwxQeHi57e3v1798/w7qGYWjs2LEKCQmRq6urKlWqpLi4OLP8119/VVRUlFxcXFSpUiWtXLkyT48VAABklJfXApL06aefKjQ0VK6urgoJCdGKFSvMsuy+i1+7dk3PPPOMQkJCZGNjo+XLl+flYeIekutERppr167pwIED2f4BAwBwO6NHj9bWrVu1f/9+7du3T1u2bNGYMWOyrN+7d28lJSWZj1q1apllAQEBGjp0qHr27JnpuiNGjNCRI0eUmJioTZs26fPPP9d///tfs7xatWr66KOP9PDDD+fdAQIAcA/KTXxes2aNevfurcmTJysxMVH79u1TdHS0WV6hQgVNmDBBTz/9dKbrDxkyRF9//bXWrVunpKQkrV27VmXKlJF080eU5s2bq2HDhjp37pwmTZqkjh076o8//sjzYwYAAP8nL68FPvnkE02cOFELFy5UUlKS4uLiFB4ebpbf7rt4nTp1NG/ePAUGBubpMeLekutERnJysp577jm5uLioSpUqOnr0qCQpNjZW48aNy/MG5pdr164VdhMAAJJmzZqloUOHqlSpUipVqpSGDBmimTNn3tG2WrdurZYtW8rX1zfT8vDwcDk5OUm6ORyira2tDh48aJa//PLLatiwoYoVK3ZH+wcAoKjITXweNmyYhg8frujoaNnZ2cnb21sPPPCAWd6tWzc9+eST8vDwyLBuWnJi1qxZqlChgmxsbBQcHKxSpUpJkjZv3qyzZ89q2LBhKlasmJo1a6b69etr3rx5+XPgAABAUt5dC9y4cUPDhw/XlClTFBkZKRsbG/n5+alcuXLm+tl9F3d0dFT//v1Vt25d2dnZ5c/B4p6Q60TGoEGDtGfPHm3cuNHij6tRo0ZatGhRnjYuL0VHR6tPnz7q37+/fH191aRJE02aNEnh4eFydXVVUFCQeZdvmjlz5sjLy0vffvutwsLC5ObmpieeeEInTpww69y4cUMDBgyQl5eXihcvrtdff12GYVjs++rVq+rbt69KliypYsWKqU6dOvrhhx/M8o0bN8rGxkbffvutIiMj5ezsrAYNGuj06dNavXq1wsLC5OHhoY4dOyo5OTn/TxYAFJDz58/r2LFjioiIMJdFRETo6NGjSkhIyHSduXPnysfHR1WqVNHEiROVmpqaq3327t1bLi4uKlOmjJKSkhQTE3MXRwAAQNGTm/h86dIl/fjjj/rnn39UqVIl+fv7q23bthbfmbKzY8cOOTk5acGCBQoICFBISIjeeOMN88azvXv3qkqVKnJwcLBoy969e+/+QAEAQKby8lrgwIEDOnXqlHbv3q2QkBAFBgaqZ8+eSkxMLMhDQhGQ60TG8uXLNW3aNNWpU0c2Njbm8ipVqujQoUN52ri89tlnn8nR0VHbtm3T9OnTZWtrqw8++ED79u3TZ599pv/97396/fXXLdZJTk7We++9p3nz5mnz5s06evSoBg4caJZPnDhRc+bM0axZs7R161adO3dOy5Yts9jG66+/ri+//FKfffaZdu/erQoVKqhJkyY6d+6cRb233npL06ZN0/bt2/X333/r2Wef1eTJk/X555/r66+/1nfffaepU6dme4xXr15VYmKixQMArFVa8tjLy8tclvb84sWLGer37dtXBw4c0L///quZM2dqypQpmjJlSq72+dFHHykpKUk//PCDunbtKm9v7ztuP5AXiN0ArE1u4vP58+dlGIaWL1+utWvX6o8//pCTk5M6d+6co32dO3dOiYmJOnjwoH7//Xdt3rxZq1ev1vjx4822pG9HWlsyu04ACgqxG0BRl5fXAmm/f65bt067du1SfHy8Dh8+rFdeeSX/DwRFSq4TGf/++69KliyZYfmlS5csEhvWqGLFipowYYJCQ0MVGhqq/v3767HHHlNISIgaNGig0aNHa/HixRbrXL9+XdOnT1eNGjX00EMPqU+fPlq/fr1ZPnnyZA0aNEitW7dWWFiYpk+fLk9PT7P80qVL+vjjj/Xuu+/qySefVOXKlTVjxgw5Oztn6I41evRoRUVFKTIyUs8995w2bdqkjz/+WJGRkapbt66eeeYZbdiwIdtjHDt2rDw9Pc1HUFBQHpw5AMgfbm5ukmRxR0fac3d39wz1H3roIZUoUUJ2dnZ69NFH9eabb95Rb0BbW1vVqFFD7u7uFslpoDAQuwFYm9zE57S6ffv2VXBwsNzc3DRy5Eht2LBBly5dyvG+Ro4cKTc3N5UpU0b9+vXTqlWrzPJb7/xMSEjI9DoBKCjEbgBFXV5eC6SVDxo0SL6+vvL19dWgQYPMWA/kVK4TGTVq1NDXX39tvk5LXnz66acWE65ao+rVq1u8XrdunRo2bKjSpUvL3d1dXbp00dmzZy2Gb3JxcVH58uXN16VKldLp06cl3fwHPnHihB555BGz3N7eXjVq1DBfHzp0SNevX1dUVJS5zMHBQQ8//LB+/fVXi/ZUrVrVfO7n5ycXFxeL8eL8/PzMfWdl0KBBSkhIMB9///13tvUBoDB5e3srMDBQ8fHx5rL4+HgFBQVZJIWzYmub6zBm4fr16xZzZACFgdgNwNrkJj57eXmZE3Pf6tYhdzNTrVq1bMurVq2qffv26fr16xZtST9BKFDQiN0Airq8vBYIDQ1lHkrkiVz/AjRmzBgNHjxYL730klJSUjRlyhQ1btxYs2fP1jvvvJMfbcwzrq6u5vMjR46oWbNmqlq1qr788kv9+OOP+vDDDyVZTgSefixW6WbiJicX5Hci/b5sbGwy3fftxoJ3cnKSh4eHxQMArFn37t31zjvv6OTJkzp58qTGjBmj559/PtO6ixcvVmJiogzD0K5duzRu3Di1adPGLE9JSdGVK1eUkpKi1NRUXblyxfxM/+uvv/Tll18qKSlJqamp2r59uz744AM1adLEXP/atWu6cuWKUlNTLbYF5CdiNwBrlJv4/MILL2jq1Kn6559/dPnyZY0aNUoNGzY078C8fv26rly5ohs3bujGjRu6cuWKmZgoW7asGjVqpFGjRik5OVnHjx/X1KlT1aJFC0lSvXr15OPjo3feeUdXr17VN998o40bN6pr164FcyKATBC7AdwP8upawNnZWZ07d9b48eN1/vx5XbhwQePHjzdjvXT77+JXr17VlStXZBiGxXUF7i+5TmTUqVNH8fHxSklJUXh4uL777juVLFlS33//fYYeD9bsxx9/VGpqqiZOnKhHH31UlSpV0vHjx3O1DU9PT5UqVUpxcXHmspSUFP3444/m6/Lly5vzcqS5fv26fvjhB1WuXPnuDwQA7nHDhg1TrVq1FBYWprCwMEVFRWnw4MGSpF69eqlXr15m3WnTpqlMmTJyd3dXp06d1Lt3b7366qtm+ejRo+Xs7Kx33nlHq1atkrOzsxo3bmyWT548WYGBgfLy8lKPHj0UGxurN9980yxv3LixnJ2dtWXLFr322mtydnbW6NGjC+AsAABgXXITn9988001bNhQ1apVU1BQkJKTkzVv3jyzvGfPnnJ2dtZ///tfTZs2Tc7OzurZs6dZPn/+fCUkJMjPz081a9ZUkyZNzLkLHRwctHLlSq1du1ZeXl7q16+f5s+frwoVKhTQmQAA4P6Ul9cCkydPVkBAgMqWLavQ0FAFBwdr0qRJZvntvouHhobK2dlZR48e1bPPPitnZ2eL7eP+YGPkV/cCKxMdHa2IiAhNnjxZkrRnzx7zdfPmzbVt2zYNGjRI//zzj86fPy8vLy/NmTNH/fv314ULF8ztLF++XK1atTJ7ZYwfP14TJkzQzJkz9cADD2jSpElauHChGjRooOXLl0uS+vfvryVLlmjmzJkqU6aMJkyYoJUrV+rQoUPy9vbWxo0b9dhjj5n7lZTpvt966y0tX77colvX7SQmJsrT01MffvihnJ2d7+IMAkDe6N69e2E3AUVAWnxLSEgocndBErsBFAbiMwpCUY3fxG4ARQHXAsiMNcVu+9yukJiYmOlyGxsbOTk5ydHR8a4bVRCqVaumSZMmafz48Ro0aJDq1aunsWPH5rqL8quvvqoTJ06oW7dusrW1VY8ePdSqVSuLyXDGjRun1NRUdenSRRcvXlSNGjX07bffytvbO68PCwAAAAAAAACAIiXXPTJsbW3NCb4zExgYqJiYGI0YMeKuJ2HF3ePOEADWhrs8kBes6a6QvEbsBlAYiM8oCEU1fhO7ARQFXAsgM9YUu3PdI2POnDkaMmSIYmJi9PDDD0uSdu7cqc8++0xDhw7Vv//+q/fee09OTk7muGkAAAAAAAAAAAB3IteJjM8++0wTJ07Us88+ay5r3ry5wsPD9Z///Efr169XmTJl9M4775DIAAAAAAAAAAAAdyXXYz9t375dkZGRGZZHRkbq+++/lyTVqVNHR48evfvWAQAAAAAAAACA+1quExlBQUGaOXNmhuUzZ85UUFCQJOns2bNMZA0AAAAAAAAAAO5aroeWeu+999S2bVutXr1aNWvWlCTt2rVLv/76q7788ktJ0g8//KB27drlbUsBAAAAAAAAAMB9J9eJjKeffloHDhzQ9OnT9fvvv0uSnnzySS1fvlxJSUmSpJdeeilvWwkAAAAAAAAAAO5LuU5kSFJISIjGjRsnSUpMTNSCBQvUrl077dq1Szdu3MjTBgIAAAAAAAAAgPtXrufISLN582Z169ZNAQEBmjhxoh577DHt2LEjL9sGAAAAAAAAAADuc7nqkXHy5EnNmTNHM2fOVGJiop599lldvXpVy5cvV+XKlfOrjQAAAAAAAAAA4D6V4x4ZzZs3V2hoqPbu3avJkyfr+PHjmjp1an62DQAAAAAAAAAA3Ody3CNj9erV6tu3r1566SVVrFgxP9sEAAAAAAAAAAAgKRc9MrZu3aqLFy+qevXqeuSRRzRt2jSdOXMmP9sGAAAAAAAAAADuczlOZDz66KOaMWOGTpw4oRdffFELFy5UQECAUlNTtXbtWl28eDE/2wkAAAAAAAAAAO5DOU5kpHF1dVWPHj20detW/fzzz3r11Vc1btw4lSxZUk8//XR+tBEAAAAAAAAAANyncp3ISC80NFQTJkzQsWPHtGDBgrxqEwAAAAAAAAAAgKS7TGSksbOzU8uWLbVy5cq82BwAAAAAAAAAAICkPEpkAAAAAAAAAAAA5AcSGQAAAAAAAAAAwGqRyAAAAAAAAAAAAFaLRAYAAAAAAAAAALBaJDIAAAAAAAAAAIDVIpEBAAAAAAAAAACsFokMAAAAAAAAAABgtewLuwEoGJ07d5aHh0dhNwMAAOQQsRsAgHsLsRsAgPxDjwwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFr2hd0AFJBZnpLz/3/+olGoTQEAADlA7AYAAAAAQBI9MgAAAAAAAAAAgBUjkQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBiRJ169fV58+feTt7S0fHx/FxsYqJSXljurervyff/5Ry5YtVbx4cfn6+urZZ5/Vv//+a5ZPmzZNNWrUkJOTk1q2bJlvxwwAwL2qIOO2m5ubxcPBwUFVq1Y1y2NiYuTo6GhR5/vvv8+/gwcA4B5QkLH6bssBALgXkMiAJGn06NHaunWr9u/fr3379mnLli0aM2bMHdW9XfnLL78sSfrrr790+PBhXblyRX379jXLAwICNHToUPXs2TOfjhYAgHtbQcbtpKQki0dYWJjat29vsY/evXtb1KlVq1b+HDgAAPeIgozVd1sOAMA9wUCRlpCQYEgyEt6XYUz//49MBAYGGkuWLDFfL1682ChTpswd1b1deXh4uDF//nzz9X//+1+jSpUqGfYzYsQIo0WLFrc/SADAfceMbwkJhd2UPJeT2F2QcTu9uLg4w87Ozvjnn3/MZd26dTP69euX4+MDANy/imr8zuy4CjJW52WsBwAgPWuK3fTIyIUvvvhC4eHhcnZ2VvHixdWoUSNdunRJMTExatmypUaOHKkSJUrIw8NDvXr10rVr18x116xZozp16sjLy0vFixdXs2bNdOjQIbP8yJEjsrGx0eLFi1W3bl05OzurZs2a+v333/XDDz+oRo0acnNz05NPPmkxDFNeOH/+vI4dO6aIiAhzWUREhI4ePaqEhIRc1c3JtgYMGKAlS5YoISFBFy5c0IIFC9S8efM8PSYAAIqqgo7b6c2cOVNPPvmkAgICLJbPnTtXPj4+qlKliiZOnKjU1NQ8OVYAAO5FBRmr8zrWAwBgrUhk5NCJEyfUoUMH9ejRQ7/++qs2btyo1q1byzAMSdL69evN5QsWLNDSpUs1cuRIc/1Lly5pwIAB2rVrl9avXy9bW1u1atUqwxf9ESNGaOjQodq9e7fs7e3VsWNHvf7665oyZYq2bNmiP/74Q8OHD8+ynVevXlViYqLF43aSkpIkSV5eXuaytOcXL17MVd2cbCsqKkqnT582x+c8f/68Bg0adNt2AgBQFOU2dhd03E5z6dIlLVy4UM8//7zF8r59++rAgQP6999/NXPmTE2ZMkVTpkzJ9hgAALiX3S52F2SszstYDwCANSORkUMnTpxQSkqKWrdurZCQEIWHh6t3795yc3OTJDk6OmrWrFmqUqWKnnrqKY0aNUoffPCBmaho06aNWrdurQoVKigiIkKzZs3Szz//rP3791vsZ+DAgWrSpInCwsLUr18//fjjjxo2bJiioqIUGRmp5557Ths2bMiynWPHjpWnp6f5CAoKuu2xpR1D+rsx0p67u7vnqu7tylNTU/X4448rKirKHEc7KipKjRs3vm07AQAoinIbuwsybqe3ZMkSubi46KmnnrJY/tBDD6lEiRKys7PTo48+qjfffFOLFi26zVEDAHDvul3sLshYnZexHgAAa0YiI4eqVaumhg0bKjw8XG3bttWMGTN0/vx5i3IXFxfzda1atZSUlKS///5bknTw4EF16NBB5cqVk4eHh0JCQiRJR48etdhP1apVzed+fn6SpPDwcItlp0+fzrKdgwYNUkJCgvlI2392vL29FRgYqPj4eHNZfHy8goKC5Onpmau6tys/d+6c/vrrL/Xt21cuLi5ycXFRbGys4uLidObMmdu2FQCAoia3sbsg43Z6n376qbp16yZ7e/ts22dry+UlAKBou13sLshYnZexHgAAa8Y3zRyys7PT2rVrtXr1alWuXFlTp05VaGioDh8+nKP1mzdvrnPnzmnGjBmKi4tTXFycJFnMoyFJDg4O5nMbG5tMl2U37rSTk5M8PDwsHjnRvXt3vfPOOzp58qROnjypMWPGZBg6Iqd1syv39fVVhQoV9OGHH+rKlSu6cuWKPvzwQwUGBsrX11eSlJKSoitXriglJUWpqam6cuVKhvMEAEBRcSexu6DidpoDBw5o+/bteu655zJsf/HixUpMTJRhGNq1a5fGjRunNm3a5PIsAABw78hJ7C7IWJ0XsR4AAGuX/S11sGBjY6OoqChFRUVp+PDhCg4O1rJlyyRJe/bs0eXLl+Xs7CxJ2rFjh9zc3BQUFKSzZ8/qwIEDmjFjhurWrStJ2rp1a6EdR2aGDRums2fPKiwsTJLUuXNnDR48WJLUq1cvSdL06dNvWzcn5StWrNArr7yi0qVLKzU1VZGRkVq5cqVZPnr0aIv5RZydnVW/fn1t3LgxH44cAIB7T0HGbenmJN9169ZVxYoVM7Rl2rRpeuGFF5SSkqLSpUurd+/eevXVV/P4iAEAuLcUZKzOi1gPAIC1szHSZqtGtuLi4rR+/Xo1btxYJUuWVFxcnDp37qzly5dr0aJF+vLLL9W8eXMNHTpUR44cUY8ePdS9e3eNHTtWqampKlmypJ588kmNGDFCR48e1ZtvvqkffvhBy5YtU8uWLXXkyBGVLVtWP/30kyIiIiRJGzdu1GOPPabz58+bk3HNmTNH/fv314ULF3LU7sTERHl6eirhfcnD+f8vfJG3HABwbzPjW0JCjnsf3iuI3QCAoqqoxu+ielwAAFhTjKNHRg55eHho8+bNmjx5shITExUcHKyJEyfqySef1KJFi9SwYUNVrFhR9erV09WrV9WhQwe99dZbkm6OFb1w4UL17dtXDz74oEJDQ/XBBx8oOjq6UI8JAAAAAAAAAABrR4+MPBATE6MLFy5o+fLlhd2UDLirEwBQFFnTXSF5jdgNACiqimr8LqrHBQCANcU4JvsGAAAAAAAAAABWi0QGAAAAAAAAAACwWsyRkQfmzJlT2E0AAAAAAAAAAKBIokcGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBqkcgAAAAAAAAAAABWi0QGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBqkcgAAAAAAAAAAABWi0QGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLXsC7sBKCA9EiQPj8JuBQAAyCliNwAAAAAAkuiRAQAAAAAAAAAArBiJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBqkcgAAAAAAAAAAABWi0QGAAAAAAAAAACwWiQyAAAAAAAAAACA1SKRAQAAAAAAAAAArBaJDAAAAAAAAAAAYLVIZAAAAAAAAAAAAKtFIgMAAAAAAAAAAFgtEhkAAAAAAAAAAMBq2Rd2AwAAAJDRiUaPKcnerrCbcc8K2L6zsJsAALjP3E3sJm4BAJA9emQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1SGQAAAAAAAAAAACrRSIDAAAAAAAAAABYLRIZAAAAAAAAAADAapHIAAAAAAAAAAAAVotEBgAAAAAAAAAAsFokMgAAAAAAAAAAgNUikQEAAAAAAAAAAKwWiQwAAAAAAAAAAGC1rDaRceTIEdnY2Cg+Pr6wm1IkXb9+XX369JG3t7d8fHwUGxurlJSUO6qb3+UAAAB3KzfXG9OmTVONGjXk5OSkli1bWpQdPXpUbm5uFg97e3s9/fTTZp3o6Gg5OTlZ1Dl+/LhZnpiYqI4dO8rDw0N+fn56++238+WYAQD3trz83h4bG6ugoCB5eHiodOnS6t+/v65du2aWE7sAANbOahMZsBQdHa3+/fvn2fZGjx6trVu3av/+/dq3b5+2bNmiMWPG3FHd/C4HAAC4W7m53ggICNDQoUPVs2fPDGVlypRRUlKS+Th37py8vLzUvn17i3rjx4+3qBcQEGCWxcbG6ty5czp69Ki2bNmiGTNmaO7cuXl7wACAe15efm/v3bu3fvvtNyUmJmrPnj3as2ePJkyYYLENYhcAwJqRyCgA6e9ysBazZs3S0KFDVapUKZUqVUpDhgzRzJkz76hufpcDAADcrdxcb7Ru3VotW7aUr6/vbbe7fPlypaamqnXr1jlqR3JyshYuXKjRo0fLy8tLlSpVUmxsLNc+AIAM8vJ7e1hYmFxdXSVJhmHI1tZWBw8ezFE7iF0AAGtQqImMNWvWqE6dOvLy8lLx4sXVrFkzHTp0KNO6NWrU0HvvvWe+btmypRwcHJSUlCRJOnbsmGxsbPTHH39IkubNm6caNWrI3d1d/v7+6tixo06fPi3pZtCuUKGCxfYkKT4+3tyGYRh66623VKZMGTk5OSkgIEB9+/bN0XGFhITo7bffVteuXeXh4aEXXnhBkrR161bVrVtXzs7OCgoKUt++fXXp0iVzvY8++kgVK1ZUsWLF5Ofnp2eeeUaSFBMTo02bNmnKlCmysbGRjY2Njhw5kqO2ZOb8+fM6duyYIiIizGURERE6evSoEhISclU3v8sBAADuVn5eb8ycOVOdOnVSsWLFLJaPHj1aPj4+ioyMtLhj9cCBA7p27VqGtuzdu/eu2gEAKFry8nt7mnHjxsnNzU0lS5bUnj17FBsba7EdYhcAwJoVaiLj0qVLGjBggHbt2qX169fL1tZWrVq1Umpqaoa69evX18aNGyXdTERs2bJFXl5e2rp1qyRp06ZNKl26tCpUqCDp5viQb7/9tvbs2aPly5fryJEjiomJkSTZ2NioR48emj17tsU+Zs+erXr16qlChQr68ssv9f777+s///mPDh48qOXLlys8PDzHx/bee++pWrVq+umnnzRs2DAdOnRITzzxhNq0aaO9e/dq0aJF2rp1q/r06SNJ2rVrl/r27atRo0bpwIEDWrNmjerVqydJmjJlimrVqqWePXvqxIkTOnHihIKCgjLd79WrV5WYmGjxuFVa8sfLy8tclvb84sWLuaqb3+UAABR1OYnduDv5db3x119/ad26dXr++ectlo8dO1aHDh3SqVOnNG7cOMXGxmrZsmVmW1xdXWVvb2/RFq57AODeURCxOy+/t6d58803lZSUpP3796tXr17y9/c3y4hdAABrV6iJjDZt2qh169aqUKGCIiIiNGvWLP3888/av39/hrrR0dHaunWrbty4ob1798rR0VGdOnUykxsbN25U/fr1zfo9evTQk08+qXLlyunRRx/VBx98oNWrV5sBPiYmRgcOHNDOnTsl3Ux8fP755+rRo4ekmxM5+vv7q1GjRipTpowefvjhTMdJzkqDBg306quvqnz58ipfvrzGjh2rTp06qX///qpYsaJq166tDz74QHPnztWVK1d09OhRubq6qlmzZgoODlZkZKTZA8TT01OOjo5ycXGRv7+//P39ZWdnl+l+x44dK09PT/ORWcLDzc1NkizuzEh77u7unqu6+V0OAEBRl5PYjbuTX9cbs2fPVmRkpKpVq2axvFatWvL09JSDg4OaNGmiF198UYsWLTLbkpycbDEBa0JCAtc9AHAPKYjYnZff228VFhamatWqmTd7SsQuAID1K9RExsGDB9WhQweVK1dOHh4eCgkJkXQziXCrunXr6uLFi/rpp5+0adMm1a9fX9HR0WYiY9OmTYqOjjbr//jjj2revLnKlCkjd3d3M8mRtu2AgAA99dRTmjVrliRp1apVunr1qtq2bStJatu2rS5fvqxy5cqpZ8+e/6+9uw+K6jz7OP7jHVTebAQxUqw4KjEaTXikaFCbknFqxtqpTbRprKax6ogz0YydpjGGGF/CGNo4E2NsqYFOx8LEjp22KWM0JLZ5a2wNaxAUReloO+JEWlC0Ii/380fHfcIjVs/Cnr3Z/X5m9g/OnoXrupfld+Di7NFvfvObHqF9K9nZ2T0+PnLkiMrKyjRkyBDvbfbs2eru7lZjY6MefPBBZWRkaPTo0Vq0aJF2796tK1eu3PbXu+5HP/qRWltbvbezZ8/esE9ycrJGjhwpj8fj3ebxeJSenq7ExERH+/r7fgAAgt3tZDf6xh/HG93d3SotLb3hbIzehIf/3yH3uHHjFBUVpSNHjvSoxcmZvwCAwHIju/vz9/bedHR0/NdrZJBdAADbBHSQMXfuXP3zn/9USUmJPv74Y3388ceSer84dlJSku655x4dPHjQO7SYMWOGqqurdeLECZ08edI7rLh8+bJmz56thIQE7d69W3/5y1+8p0R+/nMvXbpUFRUV+ve//63S0lItWLBAgwYNkiSlp6ervr5eO3bsUFxcnFauXKkZM2aoo6Pjtnq7fhGt69ra2rR8+XJ5PB7v7ciRIzp58qQyMzMVHx+vTz75ROXl5UpLS9Nzzz2ne+65Ry0tLY7WNCYmRgkJCT1uvXn88ce1efNmNTU1qampSVu2bLnpL+K32tff9wMAEMxuN7vRN06ONzo7O3X16lV1dnaqu7tbV69eveH49MCBA7pw4YK+/e1v99je0tKiyspKXblyRV1dXaqqqtLOnTs1f/58SdKgQYO0YMECrV+/Xq2trTp58qReeeUVjn0AYABxK7v76/f2trY2lZaWqqWlRcYY1dTUaNOmTZo9e7YksgsAMDBE3noX/2hublZ9fb1KSkqUl5cnSd7rXdzMzJkz9e677+rQoUPavHmzhg4dqqysLG3evFlpaWkaO3asJOn48eNqbm5WUVGR9xTPv/71rzd8vjlz5mjw4MF67bXXtG/fPv3pT3/qcX9cXJzmzp2ruXPnqqCgQOPHj1dNTY3uvfdex/3ee++9qqur817DozeRkZHKz89Xfn6+CgsLlZSUpHfeeUff/OY3FR0dra6uLsdf92bWr1+v5uZmZWVlSZIee+wxPfPMM5KkFStWSJJ27tx5y33duB8AAKCvnBz7bNq0SRs2bPA+Ni4ursf12qT/XOT7W9/61g3/6drR0aENGzZo4cKFkqRRo0bpJz/5ifesX0navn27li9frpEjRyouLk6rVq3Sd7/73f5vGgAwoPXX7+1hYWH61a9+pbVr16q9vV0pKSmaP3++N+vILgDAQBBmjDGB+MLd3d1KSUnR1772NRUWFurMmTN6+umnvWdPTJ48WV/60pdUXV2tyZMnS5J++9vfav78+Ro2bJjOnTsnSVq9erW2b9+uhx9+WOXl5ZKkzz77TCNHjtSTTz6pFStW6OjRo/rBD36gEydO9Ph8krRu3ToVFxcrMzOzx7U5ysrK1NXVpZycHA0aNEilpaX68Y9/rLNnz+oLX/jCf+1t1KhRWr16tVavXu3d9umnn+rLX/6yvve972np0qUaPHiw6urqdODAAW3fvl1vvvmmTp8+rRkzZig5OVmVlZVatWqVPv30U02YMEHLli2Tx+PRG2+8oSFDhmjo0KE9TvW8mYsXLyoxMVGtra38hycAIGgEc75d7+34/9yr+Mjer4mFWxvx4aFAlwAA+H+CNb/7I7vJLQCAjWzK7oC9tVR4eLgqKip0+PBh3X333VqzZo1eeuml//qYvLw8dXd397io96xZs9TV1dXj+hjDhg1TWVmZ9uzZo7vuuktFRUUqLi7u9XM+8cQTunbtmh5//PEe25OSklRSUqLp06dr0qRJevvtt/X73//+lkOMm5k0aZL++Mc/6sSJE8rLy9OUKVP03HPPacSIEd6vt3fvXj3wwAPKysrSzp07VV5ergkTJkiS1q5dq4iICN11110aNmxYr9cRAQAAAAAAAAAg2ATsjAxbvPfee/rqV7+qs2fPKjU1NdDl9DubpmYAAPSXYM43zsjoH/xnKwDYJ1jzmzMyAADByqbsDtg1MgKtvb1dn332mZ5//nk9/PDDQTnEAAAAAAAAAABgoAvYW0sFWnl5uTIyMtTS0qKtW7fe9uPee+89DRky5KY3AAAAAAAAAADQf0L2jIwlS5ZoyZIljh+XnZ0tj8fT7/UAAAAAAAAAAIAbhewgw1dxcXEaM2ZMoMsAAAAAAAAAACAkhOxbSwEAAAAAAAAAAPsxyAAAAAAAAAAAANZikAEAAAAAAAAAAKzFIAMAAAAAAAAAAFiLQQYAAAAAAAAAALAWgwwAAAAAAAAAAGAtBhkAAAAAAAAAAMBaDDIAAAAAAAAAAIC1GGQAAAAAAAAAAABrMcgAAAAAAAAAAADWYpABAAAAAAAAAACsxSADAAAAAAAAAABYi0EGAAAAAAAAAACwFoMMAAAAAAAAAABgLQYZAAAAAAAAAADAWgwyAAAAAAAAAACAtRhkAAAAAAAAAAAAazHIAAAAAAAAAAAA1mKQAQAAAAAAAAAArBUZ6AIAAABwo7S331VCQkKgywAAALeJ7AYAwH84IwMAAAAAAAAAAFiLQQYAAAAAAAAAALAWgwwAAAAAAAAAAGAtBhkAAAAAAAAAAMBaDDIAAAAAAAAAAIC1GGQAAAAAAAAAAABrMcgAAAAAAAAAAADWYpABAAAAAAAAAACsxSADAAAAAAAAAABYi0EGAAAAAAAAAACwFoMMAAAAAAAAAABgLQYZAAAAAAAAAADAWgwyAAAAAAAAAACAtRhkAAAAAAAAAAAAazHIAAAAAAAAAAAA1ooMdAHwL2OMJOnixYsBrgQAgP5zPdeu51wwIbsBAMEqWPOb7AYABCubsptBRpBrbm6WJKWnpwe4EgAA+t+lS5eUmJgY6DL6FdkNAAh2wZbfZDcAINjZkN0MMoLc0KFDJUlnzpwJ+Debv1y8eFHp6ek6e/asEhISAl2OX9BjcKDH4ECPdjDG6NKlSxoxYkSgS+l3oZDdthkI3/PBhjV3F+vtPta8d8Ga32S3+3iNuYv1dh9r7j7WvHc2ZTeDjCAXHv6fy6AkJiYG/YswISGBHoMAPQYHegwOtvcYrH8oCKXsto3t3/PBiDV3F+vtPtb8RsGY32R34PAacxfr7T7W3H2s+Y1syW4u9g0AAAAAAAAAAKzFIAMAAAAAAAAAAFiLQUaQi4mJUWFhoWJiYgJdit/QY3Cgx+BAj8EhFHq0GevvPtbcfay5u1hv97HmoYXn232subtYb/ex5u5jze0XZowxgS4CAAAAAAAAAACgN5yRAQAAAAAAAAAArMUgAwAAAAAAAAAAWItBBgAAAAAAAAAAsBaDDAAAAAAAAAAAYC0GGUHg1Vdf1ahRoxQbG6ucnBwdOnTov+6/Z88ejR8/XrGxsZo4caIqKytdqtR3Tnqsra3V/PnzNWrUKIWFhWnbtm3uFdoHTnosKSlRXl6ekpOTlZycrPz8/Fs+7zZw0uPevXuVnZ2tpKQkDR48WJMnT9Yvf/lLF6v1jdPX43UVFRUKCwvTN77xDf8W2A+c9FhWVqawsLAet9jYWBer9Y3T57GlpUUFBQVKS0tTTEyMxo4da/3PVic9zpo164bnMSwsTA899JCLFQeXUMhu24RCztomFDLRJqGQXbZxuubbtm3TuHHjFBcXp/T0dK1Zs0ZXr151qVr0FdntPrLbXeS2+8hud5HbQcBgQKuoqDDR0dHm9ddfN7W1teb73/++SUpKMufPn+91/w8++MBERESYrVu3mrq6OvPss8+aqKgoU1NT43Llt89pj4cOHTJr16415eXlZvjw4ebll192t2AfOO3x0UcfNa+++qqprq42x44dM0uWLDGJiYnm73//u8uV3z6nPb777rtm7969pq6uzjQ0NJht27aZiIgIs2/fPpcrv31Oe7yusbHR3HnnnSYvL8/MmzfPnWJ95LTH0tJSk5CQYM6dO+e9NTU1uVy1M057bG9vN9nZ2WbOnDnm/fffN42NjebgwYPG4/G4XPntc9pjc3Nzj+fw6NGjJiIiwpSWlrpbeJAIhey2TSjkrG1CIRNtEgrZZRuna757924TExNjdu/ebRobG81bb71l0tLSzJo1a1yuHL4gu91HdruL3HYf2e0ucjs4MMgY4KZOnWoKCgq8H3d1dZkRI0aYF198sdf9H3nkEfPQQw/12JaTk2OWL1/u1zr7wmmPn5eRkTEgBhl96dEYYzo7O018fLz5xS9+4a8S+6yvPRpjzJQpU8yzzz7rj/L6hS89dnZ2mmnTppmf//znZvHixdYf/DntsbS01CQmJrpUXf9w2uNrr71mRo8eba5du+ZWiX3W19fjyy+/bOLj401bW5u/SgxqoZDdtgmFnLVNKGSiTUIhu2zjdM0LCgrMAw880GPbU089ZaZPn+7XOtE/yG73kd3uIrfdR3a7i9wODry11AB27do1HT58WPn5+d5t4eHhys/P10cffdTrYz766KMe+0vS7Nmzb7p/oPnS40DTHz1euXJFHR0dGjp0qL/K7JO+9miMUVVVlerr6zVjxgx/luozX3t84YUXlJKSoieeeMKNMvvE1x7b2tqUkZGh9PR0zZs3T7W1tW6U6xNfevzd736n3NxcFRQUKDU1VXfffbe2bNmirq4ut8p2pD9+5uzatUsLFy7U4MGD/VVm0AqF7LZNKOSsbUIhE20SCtllG1/WfNq0aTp8+LD3bSxOnz6tyspKzZkzx5Wa4Tuy231kt7vIbfeR3e4it4NHZKALgO8uXLigrq4upaam9tiempqq48eP9/qYpqamXvdvamryW5194UuPA01/9PjDH/5QI0aMuOFg2Ra+9tja2qo777xT7e3tioiI0I4dO/Tggw/6u1yf+NLj+++/r127dsnj8bhQYd/50uO4ceP0+uuva9KkSWptbVVxcbGmTZum2tpajRw50o2yHfGlx9OnT+udd97Rd77zHVVWVqqhoUErV65UR0eHCgsL3Sjbkb7+zDl06JCOHj2qXbt2+avEoBYK2W2bUMhZ24RCJtokFLLLNr6s+aOPPqoLFy7o/vvvlzFGnZ2dWrFihZ555hk3SkYfkN3uI7vdRW67j+x2F7kdPBhkAANcUVGRKioqdPDgwQFxEWUn4uPj5fF41NbWpqqqKj311FMaPXq0Zs2aFejS+uzSpUtatGiRSkpKdMcddwS6HL/Jzc1Vbm6u9+Np06YpKytLP/3pT7Vx48YAVtZ/uru7lZKSop/97GeKiIjQfffdp3/84x966aWXgvKActeuXZo4caKmTp0a6FIAVwRzztoiVDLRJqGWXTY4ePCgtmzZoh07dignJ0cNDQ168skntXHjRq1fvz7Q5QFBhez2L3I7MMhud5HbdmKQMYDdcccdioiI0Pnz53tsP3/+vIYPH97rY4YPH+5o/0DzpceBpi89FhcXq6ioSG+//bYmTZrkzzL7xNcew8PDNWbMGEnS5MmTdezYMb344otWDjKc9njq1Cn97W9/09y5c73buru7JUmRkZGqr69XZmamf4t2qD9ej1FRUZoyZYoaGhr8UWKf+dJjWlqaoqKiFBER4d2WlZWlpqYmXbt2TdHR0X6t2am+PI+XL19WRUWFXnjhBX+WGNRCIbttEwo5a5tQyESbhEJ22caXNV+/fr0WLVqkpUuXSpImTpyoy5cva9myZVq3bp3Cw3nXZ1uR3e4ju91FbruP7HYXuR08WPUBLDo6Wvfdd5+qqqq827q7u1VVVdXjP6A/Lzc3t8f+knTgwIGb7h9ovvQ40Pja49atW7Vx40bt27dP2dnZbpTqs/56Hru7u9Xe3u6PEvvMaY/jx49XTU2NPB6P9/b1r39dX/nKV+TxeJSenu5m+belP57Hrq4u1dTUKC0tzV9l9okvPU6fPl0NDQ3eg3dJOnHihNLS0qw8mOzL87hnzx61t7frscce83eZQSsUsts2oZCztgmFTLRJKGSXbXxZ8ytXrtzwR4/rf4wyxvivWPQZ2e0+sttd5Lb7yG53kdtBJKCXGkefVVRUmJiYGFNWVmbq6urMsmXLTFJSkmlqajLGGLNo0SLz9NNPe/f/4IMPTGRkpCkuLjbHjh0zhYWFJioqytTU1ASqhVty2mN7e7uprq421dXVJi0tzaxdu9ZUV1ebkydPBqqFW3LaY1FRkYmOjja//vWvzblz57y3S5cuBaqFW3La45YtW8z+/fvNqVOnTF1dnSkuLjaRkZGmpKQkUC3cktMe/7/FixebefPmuVStb5z2uGHDBvPWW2+ZU6dOmcOHD5uFCxea2NhYU1tbG6gWbslpj2fOnDHx8fFm1apVpr6+3rz55psmJSXFbNq0KVAt3JKv36v333+/WbBggdvlBp1QyG7bhELO2iYUMtEmoZBdtnG65oWFhSY+Pt6Ul5eb06dPm/3795vMzEzzyCOPBKoFOEB2u4/sdhe57T6y213kdnBgkBEEXnnlFfPFL37RREdHm6lTp5o///nP3vtmzpxpFi9e3GP/N954w4wdO9ZER0ebCRMmmD/84Q8uV+yckx4bGxuNpBtuM2fOdL9wB5z0mJGR0WuPhYWF7hfugJMe161bZ8aMGWNiY2NNcnKyyc3NNRUVFQGo2hmnr8fPGygHf056XL16tXff1NRUM2fOHPPJJ58EoGpnnD6PH374ocnJyTExMTFm9OjRZvPmzaazs9Plqp1x2uPx48eNJLN//36XKw1OoZDdtgmFnLVNKGSiTUIhu2zjZM07OjrM888/bzIzM01sbKxJT083K1euNP/617/cLxw+IbvdR3a7i9x2H9ntLnJ74AszhvNhAAAAAAAAAACAnbhGBgAAAAAAAAAAsBaDDAAAAAAAAAAAYC0GGQAAAAAAAAAAwFoMMgAAAAAAAAAAgLUYZAAAAAAAAAAAAGsxyAAAAAAAAAAAANZikAEAAAAAAAAAAKzFIAMAAAAAAAAAAFiLQQYAAAAAAAAAALAWgwwAAAAAAAAAAGAtBhkAAAAAAAAAAMBaDDIAAAAAAAAAAIC1/hdSEgsSbWgKrgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1600x500 with 3 Axes>"
       ]
@@ -568,13 +624,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa65e138",
+   "id": "24956bd9",
    "metadata": {
     "papermill": {
-     "duration": 0.003195,
-     "end_time": "2026-04-25T22:47:55.791791+00:00",
+     "duration": 0.003427,
+     "end_time": "2026-04-26T00:59:50.346130+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:55.788596+00:00",
+     "start_time": "2026-04-26T00:59:50.342703+00:00",
      "status": "completed"
     },
     "tags": []
@@ -588,19 +644,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "c398c28f",
+   "id": "65d9d78e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:47:55.799357Z",
-     "iopub.status.busy": "2026-04-25T22:47:55.799159Z",
-     "iopub.status.idle": "2026-04-25T22:48:07.663196Z",
-     "shell.execute_reply": "2026-04-25T22:48:07.662555Z"
+     "iopub.execute_input": "2026-04-26T00:59:50.353964Z",
+     "iopub.status.busy": "2026-04-26T00:59:50.353735Z",
+     "iopub.status.idle": "2026-04-26T01:00:02.841745Z",
+     "shell.execute_reply": "2026-04-26T01:00:02.840967Z"
     },
     "papermill": {
-     "duration": 11.868763,
-     "end_time": "2026-04-25T22:48:07.663781+00:00",
+     "duration": 12.492696,
+     "end_time": "2026-04-26T01:00:02.842283+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:47:55.795018+00:00",
+     "start_time": "2026-04-26T00:59:50.349587+00:00",
      "status": "completed"
     },
     "tags": []
@@ -609,12 +665,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8276e58154be4b79be454000483941ef",
+       "model_id": "28120ddaec6a4dc98af1d11cc2c1363d",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "config.json:   0%|          | 0.00/660 [00:00<?, ?B/s]"
+       "config.json:   0%|          | 0.00/661 [00:00<?, ?B/s]"
       ]
      },
      "metadata": {},
@@ -623,7 +679,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7c4e1be8f3894bada5435981f2062cc5",
+       "model_id": "e580c60c036540b1a405210346088404",
        "version_major": 2,
        "version_minor": 0
       },
@@ -637,7 +693,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7f98669d5e64269a1461e9b151aa312",
+       "model_id": "b39e87fe3778477f909c1afeca1bf519",
        "version_major": 2,
        "version_minor": 0
       },
@@ -651,7 +707,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58711a6139e14b5e824ce7717eb87bb5",
+       "model_id": "22fb3099714f4fc0a7263469a5d37779",
        "version_major": 2,
        "version_minor": 0
       },
@@ -665,7 +721,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f69304571dcf4176b37a7bf5fbff895c",
+       "model_id": "7dedf106768641e284dfe3a69d3681b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -680,18 +736,46 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loading Qwen/Qwen2.5-1.5B-Instruct (4-bit quantized, CUDA)...\n"
+      "Loading Qwen/Qwen2.5-3B-Instruct (dtype=torch.bfloat16, attn=sdpa)...\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f0d3058737d44ef98442b1576d0626e1",
+       "model_id": "ba498a2526854a97b4ad6131d4b476cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors.index.json:   0%|          | 0.00/35.6k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4029d6b81ac34c178bfd4238a297046c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf10a5c9efc5488f9bc3a88e14f8d75d",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "model.safetensors:   0%|          | 0.00/3.09G [00:00<?, ?B/s]"
+       "Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -700,12 +784,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1c3cba90df624bfaa6b8fd950e834ed1",
+       "model_id": "71fbda7384cd4d59abb1205d66c5a6dd",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Loading weights:   0%|          | 0/338 [00:00<?, ?it/s]"
+       "Loading weights:   0%|          | 0/434 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -714,7 +798,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2161c6ddcfb6452a9ec8dab562ef47cb",
+       "model_id": "cca2d67214454b03bd25edbcf9b3f520",
        "version_major": 2,
        "version_minor": 0
       },
@@ -729,94 +813,72 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model loaded. dtype=torch.bfloat16\n",
-      "Device: cuda:0\n",
-      "CUDA memory: 1.15 GB\n"
+      "Model loaded. dtype=torch.bfloat16 device=cuda:0\n",
+      "CUDA memory: 6.17 GB\n"
      ]
     }
    ],
    "source": [
-    "# Cell 7: Load model (4-bit on CUDA Colab; fp16/fp32 fallback if bitsandbytes missing)\n",
+    "# Cell 7: Load model (Qwen2.5-3B bf16 on CUDA + flash-attn-2; fp16/fp32 fallback)\n",
     "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
     "\n",
-    "MODEL_NAME = \"Qwen/Qwen2.5-1.5B-Instruct\"\n",
+    "MODEL_NAME = \"Qwen/Qwen2.5-3B-Instruct\"\n",
     "\n",
     "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)\n",
+    "if tokenizer.pad_token is None:\n",
+    "    tokenizer.pad_token = tokenizer.eos_token\n",
+    "tokenizer.padding_side = \"left\"\n",
     "\n",
-    "_use_4bit = False\n",
-    "try:\n",
-    "    from transformers.utils import is_bitsandbytes_available\n",
-    "except Exception:  # older transformers\n",
-    "    def is_bitsandbytes_available():\n",
-    "        try:\n",
-    "            import bitsandbytes  # noqa: F401\n",
-    "            return True\n",
-    "        except ImportError:\n",
-    "            return False\n",
     "\n",
-    "if torch.cuda.is_available() and is_bitsandbytes_available():\n",
-    "    from transformers import BitsAndBytesConfig\n",
-    "    _use_4bit = True\n",
+    "def _has_flash_attn():\n",
+    "    try:\n",
+    "        import flash_attn  # noqa: F401\n",
+    "        return torch.cuda.is_available()\n",
+    "    except Exception:\n",
+    "        return False\n",
     "\n",
-    "if _use_4bit:\n",
-    "    print(f\"Loading {MODEL_NAME} (4-bit quantized, CUDA)...\")\n",
-    "    bnb_config = BitsAndBytesConfig(\n",
-    "        load_in_4bit=True,\n",
-    "        bnb_4bit_quant_type=\"nf4\",\n",
-    "        bnb_4bit_compute_dtype=torch.float16,\n",
-    "        bnb_4bit_use_double_quant=True,\n",
-    "    )\n",
-    "    model = AutoModelForCausalLM.from_pretrained(\n",
-    "        MODEL_NAME,\n",
-    "        trust_remote_code=True,\n",
-    "        quantization_config=bnb_config,\n",
-    "        device_map=\"auto\",\n",
-    "    )\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    dtype = torch.bfloat16\n",
+    "    attn_impl = \"flash_attention_2\" if _has_flash_attn() else \"sdpa\"\n",
+    "elif getattr(torch.backends, \"mps\", None) and torch.backends.mps.is_available():\n",
+    "    dtype, attn_impl = torch.float16, \"sdpa\"\n",
     "else:\n",
-    "    print(\n",
-    "        f\"Loading {MODEL_NAME} without 4-bit (bitsandbytes/CUDA unavailable).\\n\"\n",
-    "        \"  On Colab: run `pip install -U bitsandbytes>=0.46.1` and use a GPU runtime.\\n\"\n",
-    "        \"  On Mac: use fp16 on MPS or fp32 on CPU.\"\n",
-    "    )\n",
-    "    dtype = torch.float16 if (torch.cuda.is_available() or getattr(torch.backends, \"mps\", None) and torch.backends.mps.is_available()) else torch.float32\n",
-    "    model = AutoModelForCausalLM.from_pretrained(\n",
-    "        MODEL_NAME,\n",
-    "        trust_remote_code=True,\n",
-    "        dtype=dtype,\n",
-    "        device_map=\"auto\" if torch.cuda.is_available() else None,\n",
-    "    )\n",
-    "    if not torch.cuda.is_available():\n",
-    "        if getattr(torch.backends, \"mps\", None) and torch.backends.mps.is_available():\n",
-    "            model = model.to(\"mps\")\n",
-    "        else:\n",
-    "            model = model.to(\"cpu\")\n",
+    "    dtype, attn_impl = torch.float32, \"eager\"\n",
+    "\n",
+    "print(f\"Loading {MODEL_NAME} (dtype={dtype}, attn={attn_impl})...\")\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    MODEL_NAME,\n",
+    "    trust_remote_code=True,\n",
+    "    dtype=dtype,\n",
+    "    attn_implementation=attn_impl,\n",
+    "    device_map=\"cuda:0\" if torch.cuda.is_available() else None,\n",
+    ")\n",
+    "if not torch.cuda.is_available():\n",
+    "    model = model.to(\"mps\") if (getattr(torch.backends, \"mps\", None) and torch.backends.mps.is_available()) else model.to(\"cpu\")\n",
     "\n",
     "model.eval()\n",
-    "print(f\"Model loaded. dtype={next(model.parameters()).dtype}\")\n",
-    "try:\n",
-    "    print(f\"Device: {model.device}\")\n",
-    "except Exception:\n",
-    "    print(\"Device: (see first parameter device)\")\n",
+    "print(f\"Model loaded. dtype={next(model.parameters()).dtype} device={next(model.parameters()).device}\")\n",
     "if torch.cuda.is_available():\n",
     "    print(f\"CUDA memory: {torch.cuda.memory_allocated()/1e9:.2f} GB\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "62b45537",
+   "execution_count": 8,
+   "id": "b7cd06a8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:48:07.673186Z",
-     "iopub.status.busy": "2026-04-25T22:48:07.672763Z",
-     "iopub.status.idle": "2026-04-25T22:48:07.687754Z",
-     "shell.execute_reply": "2026-04-25T22:48:07.686981Z"
+     "iopub.execute_input": "2026-04-26T01:00:02.867320Z",
+     "iopub.status.busy": "2026-04-26T01:00:02.866901Z",
+     "iopub.status.idle": "2026-04-26T01:00:02.885333Z",
+     "shell.execute_reply": "2026-04-26T01:00:02.884669Z"
     },
     "papermill": {
-     "duration": 0.020105,
-     "end_time": "2026-04-25T22:48:07.688306+00:00",
+     "duration": 0.037452,
+     "end_time": "2026-04-26T01:00:02.885835+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:48:07.668201+00:00",
+     "start_time": "2026-04-26T01:00:02.848383+00:00",
      "status": "completed"
     },
     "tags": []
@@ -826,7 +888,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LLM agent functions defined.\n"
+      "LLM agent functions defined (batched).\n"
      ]
     }
    ],
@@ -848,15 +910,15 @@
     "  \"notes\": \"strategy notes\"\n",
     "}\n",
     "\n",
-    "TOOLS (cost in API budget, total=100):\n",
-    "- query_trends(niche)                            cost=1  trending topics+tags for niche\n",
-    "- query_audience(segment_id)                     cost=2  segment topic affinities + active hours\n",
-    "- query_competitor(competitor_id, window_days)   cost=2  competitor recent posts\n",
-    "- query_tag_history(tag)                         cost=1  your past signals (watch/sends/saves/likes) for a tag\n",
-    "- predict_engagement(scheduled_actions)          cost=3  simulate a plan WITHOUT committing\n",
-    "- draft_review(scheduled_actions)                cost=3  AI review of a draft plan\n",
-    "- query_creator_pool()                           cost=1  list collab partners with audience overlap\n",
-    "- propose_collab(partner_id, content_type, hour) cost=5  co-author the post at that hour (max 2/month)\n",
+    "TOOLS:\n",
+    "- query_trends(niche)                            trending topics+tags for niche\n",
+    "- query_audience(segment_id)                     segment topic affinities + active hours\n",
+    "- query_competitor(competitor_id, window_days)   competitor recent posts\n",
+    "- query_tag_history(tag)                         your past signals (watch/sends/saves/likes) for a tag\n",
+    "- predict_engagement(scheduled_actions)          simulate a plan WITHOUT committing\n",
+    "- draft_review(scheduled_actions)                AI review of a draft plan\n",
+    "- query_creator_pool()                           list collab partners with audience overlap\n",
+    "- propose_collab(partner_id, content_type, hour) co-author the post at that hour (max 2/month)\n",
     "\n",
     "ACTION SCHEMA:\n",
     "- hour:         0..23 (unlisted hours = rest)\n",
@@ -895,6 +957,21 @@
     "            f\"Plan your actions (JSON only):\")\n",
     "\n",
     "\n",
+    "def is_well_formed_response(text):\n",
+    "    try:\n",
+    "        t = text.strip()\n",
+    "        if \"```\" in t:\n",
+    "            t = \"\\n\".join(l for l in t.split(\"\\n\") if not l.strip().startswith(\"```\")).strip()\n",
+    "        s, e = t.find(\"{\"), t.rfind(\"}\") + 1\n",
+    "        d = json.loads(t[s:e])\n",
+    "        for tc in d.get(\"tool_calls\", []):\n",
+    "            if not isinstance(tc, dict) or not isinstance(tc.get(\"arguments\", {}), dict):\n",
+    "                return False\n",
+    "        return True\n",
+    "    except Exception:\n",
+    "        return False\n",
+    "\n",
+    "\n",
     "def parse_model_output(text):\n",
     "    text = text.strip()\n",
     "    if \"```\" in text:\n",
@@ -905,23 +982,32 @@
     "        text = text[start:end]\n",
     "    try:\n",
     "        data = json.loads(text)\n",
-    "        tool_calls = [ToolCall(name=tc[\"name\"], arguments=tc.get(\"arguments\", {}))\n",
-    "                      for tc in data.get(\"tool_calls\", []) if isinstance(tc, dict) and \"name\" in tc]\n",
-    "        scheduled = []\n",
-    "        for a in data.get(\"scheduled_actions\", []):\n",
-    "            try:\n",
-    "                scheduled.append(ScheduledAction(**a))\n",
-    "            except Exception:\n",
-    "                # Same as original bare `except:`: skip invalid scheduled_actions entries\n",
-    "                pass\n",
-    "        return ViraltestAction(\n",
-    "            tool_calls=tool_calls,\n",
-    "            scheduled_actions=scheduled,\n",
-    "            notes=data.get(\"notes\"),\n",
-    "        )\n",
     "    except Exception:\n",
-    "        # Same behavior as original bare `except:`: any parse/validation failure -> empty action\n",
     "        return ViraltestAction(scheduled_actions=[])\n",
+    "    tool_calls = []\n",
+    "    for tc in data.get(\"tool_calls\", []):\n",
+    "        if not isinstance(tc, dict) or \"name\" not in tc:\n",
+    "            continue\n",
+    "        args = tc.get(\"arguments\", {})\n",
+    "        if isinstance(args, list) and args and isinstance(args[0], dict):\n",
+    "            args = args[0]\n",
+    "        if not isinstance(args, dict):\n",
+    "            continue\n",
+    "        try:\n",
+    "            tool_calls.append(ToolCall(name=tc[\"name\"], arguments=args))\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "    scheduled = []\n",
+    "    for a in data.get(\"scheduled_actions\", []):\n",
+    "        try:\n",
+    "            scheduled.append(ScheduledAction(**a))\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "    return ViraltestAction(\n",
+    "        tool_calls=tool_calls,\n",
+    "        scheduled_actions=scheduled,\n",
+    "        notes=data.get(\"notes\"),\n",
+    "    )\n",
     "\n",
     "\n",
     "def _infer_model_device(m):\n",
@@ -935,77 +1021,109 @@
     "    return torch.device(\"cpu\")\n",
     "\n",
     "\n",
-    "def generate_action(mdl, tok, obs, history, temperature=0.7, debug=True):\n",
-    "    prompt = format_obs(obs)\n",
-    "    messages = [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}]\n",
-    "    messages.extend(history[-14:])\n",
-    "    messages.append({\"role\": \"user\", \"content\": prompt})\n",
-    "    text_input = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
-    "    inputs = tok(text_input, return_tensors=\"pt\").to(_infer_model_device(mdl))\n",
+    "def _build_chat(history, prompt):\n",
+    "    msgs = [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}]\n",
+    "    msgs.extend(history[-14:])\n",
+    "    msgs.append({\"role\": \"user\", \"content\": prompt})\n",
+    "    return msgs\n",
+    "\n",
+    "\n",
+    "def _batched_generate(mdl, tok, prompts, temperature=0.7, max_new_tokens=512):\n",
+    "    enc = tok(prompts, return_tensors=\"pt\", padding=True, truncation=False).to(_infer_model_device(mdl))\n",
     "    with torch.no_grad():\n",
-    "        out = mdl.generate(**inputs, max_new_tokens=512, temperature=temperature,\n",
-    "                           do_sample=True, top_p=0.9, pad_token_id=tok.eos_token_id)\n",
-    "    resp = tok.decode(out[0][inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True)\n",
-    "    if debug:\n",
-    "        print(\"=\" * 60)\n",
-    "        print(f\"[LLM PROMPT] tokens={inputs['input_ids'].shape[1]}\")\n",
-    "        print(prompt)\n",
-    "        print(\"-\" * 60)\n",
-    "        print(f\"[LLM RESPONSE] tokens={out.shape[1] - inputs['input_ids'].shape[1]}\")\n",
-    "        print(resp)\n",
-    "        print(\"=\" * 60)\n",
-    "    return resp, parse_model_output(resp)\n",
+    "        out = mdl.generate(\n",
+    "            **enc, max_new_tokens=max_new_tokens, temperature=temperature,\n",
+    "            do_sample=True, top_p=0.9, pad_token_id=tok.pad_token_id,\n",
+    "        )\n",
+    "    resps = tok.batch_decode(out[:, enc[\"input_ids\"].shape[1]:], skip_special_tokens=True)\n",
+    "    return resps, enc[\"input_ids\"].shape[1]\n",
     "\n",
     "\n",
-    "def run_llm_episode(mdl, tok, task, seed=42, verbose=False, debug_llm=True):\n",
-    "    env = ViraltestEnvironment()\n",
-    "    obs = env.reset(task=task, seed=seed)\n",
-    "    rewards, energies = [], [obs.creator_energy]\n",
-    "    history, pairs = [], []\n",
+    "def run_llm_episodes_batched(mdl, tok, tasks_seeds, verbose=True):\n",
+    "    \"\"\"Run N episodes in parallel. tasks_seeds: list of (task, seed). One batched generate per day.\"\"\"\n",
+    "    n = len(tasks_seeds)\n",
+    "    envs = [ViraltestEnvironment() for _ in range(n)]\n",
+    "    obss = [envs[i].reset(task=t, seed=s) for i, (t, s) in enumerate(tasks_seeds)]\n",
+    "    histories = [[] for _ in range(n)]\n",
+    "    rewards = [[] for _ in range(n)]\n",
+    "    energies = [[obs.creator_energy] for obs in obss]\n",
+    "    pairs = [[] for _ in range(n)]\n",
+    "    done_mask = [obs.done for obs in obss]\n",
+    "    rest_resp = '{\"scheduled_actions\": []}'\n",
+    "\n",
     "    for day in range(1, TASK_HORIZON + 1):\n",
-    "        if obs.done: break\n",
-    "        if debug_llm:\n",
-    "            print(f\"\\n>>> Day {day} | task={task} | energy={obs.creator_energy:.2f}\")\n",
-    "        resp, action = generate_action(mdl, tok, obs, history, debug=debug_llm)\n",
-    "        prompt = format_obs(obs)\n",
-    "        pairs.append({\"prompt\": prompt, \"response\": resp})\n",
-    "        obs = env.step(action)\n",
-    "        r = obs.reward or 0.0\n",
-    "        rewards.append(r)\n",
-    "        energies.append(obs.creator_energy)\n",
-    "        history.extend([{\"role\": \"user\", \"content\": prompt},\n",
-    "                        {\"role\": \"assistant\", \"content\": resp}])\n",
-    "        if verbose:\n",
-    "            n_p = len([s for s in action.scheduled_actions if s.action_type==\"post\"])\n",
-    "            print(f\"    Day {day:2d}: r={r:.4f} e={obs.creator_energy:.2f} posts={n_p} tools={len(action.tool_calls)}\")\n",
-    "        if obs.done: break\n",
-    "    gs = (obs.metadata or {}).get(\"grader_score\", 0.0)\n",
-    "    # Per-step credit assignment: G_t = r_t + gamma * G_{t+1}, terminal = grader_score * w\n",
+    "        active = [i for i in range(n) if not done_mask[i] and obss[i].creator_energy > 0.25]\n",
+    "        rest = [i for i in range(n) if not done_mask[i] and obss[i].creator_energy <= 0.25]\n",
+    "        if not active and not rest:\n",
+    "            break\n",
+    "\n",
+    "        resps_by_idx = {}\n",
+    "        if active:\n",
+    "            prompts = [format_obs(obss[i]) for i in active]\n",
+    "            chats = [_build_chat(histories[i], p) for i, p in zip(active, prompts)]\n",
+    "            texts = [tok.apply_chat_template(c, tokenize=False, add_generation_prompt=True) for c in chats]\n",
+    "            resps, ptok = _batched_generate(mdl, tok, texts)\n",
+    "            if verbose:\n",
+    "                print(f\"  D{day:2d}: batch={len(active)} rest={len(rest)} prompt_tok={ptok}\")\n",
+    "            for j, i in enumerate(active):\n",
+    "                resps_by_idx[i] = (resps[j], prompts[j])\n",
+    "        for i in rest:\n",
+    "            resps_by_idx[i] = (rest_resp, format_obs(obss[i]))\n",
+    "\n",
+    "        for i in range(n):\n",
+    "            if done_mask[i] or i not in resps_by_idx:\n",
+    "                continue\n",
+    "            resp, prompt = resps_by_idx[i]\n",
+    "            action = parse_model_output(resp)\n",
+    "            pairs[i].append({\"prompt\": prompt, \"response\": resp})\n",
+    "            obss[i] = envs[i].step(action)\n",
+    "            r = obss[i].reward or 0.0\n",
+    "            rewards[i].append(r)\n",
+    "            energies[i].append(obss[i].creator_energy)\n",
+    "            histories[i].extend([\n",
+    "                {\"role\": \"user\", \"content\": prompt},\n",
+    "                {\"role\": \"assistant\", \"content\": resp},\n",
+    "            ])\n",
+    "            if obss[i].done:\n",
+    "                done_mask[i] = True\n",
+    "\n",
     "    GAMMA, TERMINAL_W = 0.95, 5.0\n",
-    "    G, returns = gs * TERMINAL_W, [0.0] * len(rewards)\n",
-    "    for t in reversed(range(len(rewards))):\n",
-    "        G = rewards[t] + GAMMA * G\n",
-    "        returns[t] = G\n",
-    "    for i, pr in enumerate(pairs):\n",
-    "        pr[\"return\"] = returns[i] if i < len(returns) else 0.0\n",
-    "    return {\"task\": task, \"grader_score\": gs, \"total_reward\": sum(rewards),\n",
-    "            \"final_energy\": obs.creator_energy, \"rewards\": rewards,\n",
-    "            \"returns\": returns, \"energies\": energies, \"pairs\": pairs,\n",
-    "            \"follower_delta\": obs.follower_count - 10000,\n",
-    "            \"burned_out\": obs.creator_energy <= 0}\n",
+    "    results = []\n",
+    "    for i, (task, seed) in enumerate(tasks_seeds):\n",
+    "        gs = (obss[i].metadata or {}).get(\"grader_score\", 0.0)\n",
+    "        rets = [0.0] * len(rewards[i])\n",
+    "        G = gs * TERMINAL_W\n",
+    "        for t in reversed(range(len(rewards[i]))):\n",
+    "            G = rewards[i][t] + GAMMA * G\n",
+    "            rets[t] = G\n",
+    "        for k, pr in enumerate(pairs[i]):\n",
+    "            pr[\"return\"] = rets[k] if k < len(rets) else 0.0\n",
+    "        results.append({\n",
+    "            \"task\": task, \"seed\": seed, \"grader_score\": gs,\n",
+    "            \"total_reward\": sum(rewards[i]), \"final_energy\": obss[i].creator_energy,\n",
+    "            \"rewards\": rewards[i], \"returns\": rets, \"energies\": energies[i],\n",
+    "            \"pairs\": pairs[i], \"follower_delta\": obss[i].follower_count - 10000,\n",
+    "            \"burned_out\": obss[i].creator_energy <= 0,\n",
+    "        })\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "def run_llm_episode(mdl, tok, task, seed=42, verbose=False):\n",
+    "    return run_llm_episodes_batched(mdl, tok, [(task, seed)], verbose=verbose)[0]\n",
+    "\n",
     "\n",
-    "print(\"LLM agent functions defined.\")"
+    "print(\"LLM agent functions defined (batched).\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2ca536ec",
+   "id": "934c8834",
    "metadata": {
     "papermill": {
-     "duration": 0.00386,
-     "end_time": "2026-04-25T22:48:07.696100+00:00",
+     "duration": 0.015722,
+     "end_time": "2026-04-26T01:00:02.908967+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:48:07.692240+00:00",
+     "start_time": "2026-04-26T01:00:02.893245+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1019,19 +1137,19 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "17cdad0c",
+   "id": "6bc18bfe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:48:07.707740Z",
-     "iopub.status.busy": "2026-04-25T22:48:07.707565Z",
-     "iopub.status.idle": "2026-04-25T22:51:33.776174Z",
-     "shell.execute_reply": "2026-04-25T22:51:33.775168Z"
+     "iopub.execute_input": "2026-04-26T01:00:02.944540Z",
+     "iopub.status.busy": "2026-04-26T01:00:02.944365Z",
+     "iopub.status.idle": "2026-04-26T01:01:27.841820Z",
+     "shell.execute_reply": "2026-04-26T01:01:27.840971Z"
     },
     "papermill": {
-     "duration": 206.073866,
-     "end_time": "2026-04-25T22:51:33.776812+00:00",
+     "duration": 84.914849,
+     "end_time": "2026-04-26T01:01:27.842785+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:48:07.702946+00:00",
+     "start_time": "2026-04-26T01:00:02.927936+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1041,1033 +1159,146 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running UNTRAINED base model on all tasks...\n",
-      "============================================================\n",
-      "\n",
-      "  Task: monthly_engage\n",
-      "\n",
-      ">>> Day 1 | task=monthly_engage | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=538\n",
-      "Day: Mon | days_elapsed=0\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 0\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=96\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"daily tips\",\n",
-      "      \"tags\": [\"daily\", \"tips\", \"inspiration\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Started with daily tips as it's a common and easy-to-share theme.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  1: r=0.2191 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 2 | task=monthly_engage | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=722\n",
-      "Day: Tue | days_elapsed=1\n",
-      "Energy: 1.00 | Followers: 10076\n",
-      "Engagement: 0.760 | Queue: 0\n",
-      "Signals: watch=0.380 sends=0.304 saves=0.076\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=158\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"weekly productivity tips\",\n",
-      "      \"tags\": [\"productivity\", \"tips\", \"success\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 18,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"carousel\",\n",
-      "      \"topic\": \"best practices for remote work\",\n",
-      "      \"tags\": [\"remote\", \"work\", \"tips\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Adjusted strategy based on engagement metrics, focusing on productivity and remote work themes.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  2: r=0.2062 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 3 | task=monthly_engage | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=968\n",
-      "Day: Wed | days_elapsed=2\n",
-      "Energy: 1.00 | Followers: 10095\n",
-      "Engagement: 0.476 | Queue: 1\n",
-      "Signals: watch=0.038 sends=0.123 saves=0.010\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=162\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 12,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"daily mindfulness exercises\",\n",
-      "      \"tags\": [\"mindfulness\", \"exercise\", \"morning\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 20,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"carousel\",\n",
-      "      \"topic\": \"top travel destinations\",\n",
-      "      \"tags\": [\"travel\", \"destinations\", \"explore\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Increased focus on mental health and travel destinations after initial productivity and remote work efforts.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  3: r=0.1769 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 4 | task=monthly_engage | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1218\n",
-      "Day: Thu | days_elapsed=3\n",
-      "Energy: 1.00 | Followers: 10195\n",
-      "Engagement: 0.651 | Queue: 1\n",
-      "Signals: watch=0.150 sends=0.150 saves=0.501\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=160\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 1,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"monthly skincare routine\",\n",
-      "      \"tags\": [\"skincare\", \"routine\", \"beauty\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 11,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"essential home decor items\",\n",
-      "      \"tags\": [\"decor\", \"home\", \"items\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Continued trend towards mental wellness, beauty routines, and practical home decor advice.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  4: r=0.2302 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 5 | task=monthly_engage | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1466\n",
-      "Day: Fri | days_elapsed=4\n",
-      "Energy: 1.00 | Followers: 10198\n",
-      "Engagement: 0.498 | Queue: 1\n",
-      "Signals: watch=0.012 sends=0.016 saves=0.002\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=167\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 13,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"carousel\",\n",
-      "      \"topic\": \"cooking techniques\",\n",
-      "      \"tags\": [\"cooking\", \"techniques\", \"recipes\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 21,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"seasonal outdoor activities\",\n",
-      "      \"tags\": [\"outdoor\", \"activities\", \"seasonal\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Shifted focus to cooking techniques and seasonal outdoor activities to align with current trends in hobbies and leisure.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  5: r=0.1732 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 6 | task=monthly_engage | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1721\n",
-      "Day: Sat | days_elapsed=5\n",
-      "Energy: 1.00 | Followers: 10319\n",
-      "Engagement: 0.642 | Queue: 1\n",
-      "Signals: watch=0.913 sends=0.304 saves=0.122\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=165\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"interview with local artists\",\n",
-      "      \"tags\": [\"artists\", \"local\", \"celebrity\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 14,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"summer fashion trends\",\n",
-      "      \"tags\": [\"fashion\", \"trends\", \"summer\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Encouraged diversity in interests by including interviews with local celebrities and discussing summer fashion trends.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  6: r=0.2156 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 7 | task=monthly_engage | energy=1.00\n"
+      "Running UNTRAINED base model on all tasks (batched)...\n",
+      "============================================================\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1974\n",
-      "Day: Sun | days_elapsed=6\n",
-      "Energy: 1.00 | Followers: 10367\n",
-      "Engagement: 0.616 | Queue: 1\n",
-      "Signals: watch=0.365 sends=0.122 saves=0.049\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=168\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 12,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"remote work productivity hacks\",\n",
-      "      \"tags\": [\"remote\", \"work\", \"hacks\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 20,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"new year's resolutions\",\n",
-      "      \"tags\": [\"resolutions\", \"goals\", \"NewYear\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Stressed about keeping up momentum so adjusted strategy to include productivity hacks and New Year's resolution-themed content.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  7: r=0.1716 e=1.00 posts=1 tools=0\n",
-      "  => grader=0.3048 reward=1.393\n",
-      "\n",
-      "  Task: monthly_strategic\n",
-      "\n",
-      ">>> Day 1 | task=monthly_strategic | energy=1.00\n"
+      "  D 1: batch=3 rest=0 prompt_tok=495\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=538\n",
-      "Day: Mon | days_elapsed=0\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 0\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=156\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"daily routine\",\n",
-      "      \"tags\": [\"morning\", \"start\", \"day\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 17,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"inspired by nature\",\n",
-      "      \"tags\": [\"nature\", \"peaceful\", \"adventure\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Starts posting daily routines and reels inspired by nature.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  1: r=0.2089 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 2 | task=monthly_strategic | energy=1.00\n"
+      "  D 2: batch=3 rest=0 prompt_tok=706\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=782\n",
-      "Day: Tue | days_elapsed=1\n",
-      "Energy: 1.00 | Followers: 10042\n",
-      "Engagement: 0.426 | Queue: 1\n",
-      "Signals: watch=0.021 sends=0.068 saves=0.128\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=157\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"how-to blog entry\",\n",
-      "      \"tags\": [\"technology\", \"tutorial\", \"infographics\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 15,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"personal development\",\n",
-      "      \"tags\": [\"growth\", \"self-improvement\", \"mindset\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Publishes a new tech tutorial and personal development story.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  2: r=0.2091 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 3 | task=monthly_strategic | energy=1.00\n"
+      "  D 3: batch=3 rest=0 prompt_tok=979\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1027\n",
-      "Day: Wed | days_elapsed=2\n",
-      "Energy: 1.00 | Followers: 10087\n",
-      "Engagement: 0.439 | Queue: 1\n",
-      "Signals: watch=0.023 sends=0.072 saves=0.136\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=154\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"motivational quote\",\n",
-      "      \"tags\": [\"quotes\", \"motivation\", \"inspiration\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 16,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"carousel\",\n",
-      "      \"topic\": \"travel destinations\",\n",
-      "      \"tags\": [\"destinations\", \"vacation\", \"explore\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Posts motivational quotes and a destination carousel.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  3: r=0.2172 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 4 | task=monthly_strategic | energy=1.00\n"
+      "  D 4: batch=3 rest=0 prompt_tok=1206\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1269\n",
-      "Day: Thu | days_elapsed=3\n",
-      "Energy: 1.00 | Followers: 10231\n",
-      "Engagement: 0.774 | Queue: 1\n",
-      "Signals: watch=1.084 sends=0.361 saves=0.144\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=156\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"science experiments\",\n",
-      "      \"tags\": [\"experiments\", \"science\", \"fun\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 17,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"history lesson\",\n",
-      "      \"tags\": [\"education\", \"history\", \"facts\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Creates two engaging posts: a science experiment reel and a history lesson story.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  4: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 5 | task=monthly_strategic | energy=1.00\n"
+      "  D 5: batch=3 rest=0 prompt_tok=1422\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1513\n",
-      "Day: Fri | days_elapsed=4\n",
-      "Energy: 1.00 | Followers: 10231\n",
-      "Engagement: 0.774 | Queue: 3\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=155\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"random facts\",\n",
-      "      \"tags\": [\"fun\", \"knowledge\", \"interesting\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 15,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"space exploration\",\n",
-      "      \"tags\": [\"exploration\", \"astronomy\", \"galaxy\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Shares random facts through a reel and publishes a space exploration story.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  5: r=0.2129 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 6 | task=monthly_strategic | energy=1.00\n"
+      "  D 6: batch=3 rest=0 prompt_tok=1665\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1756\n",
-      "Day: Sat | days_elapsed=5\n",
-      "Energy: 1.00 | Followers: 10337\n",
-      "Engagement: 0.847 | Queue: 3\n",
-      "Signals: watch=0.532 sends=0.426 saves=0.106\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=158\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"healthy living tips\",\n",
-      "      \"tags\": [\"wellness\", \"nutrition\", \"tips\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 16,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"local events\",\n",
-      "      \"tags\": [\"events\", \"community\", \"local\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Creates two interesting posts: a healthy living tip reel and a local event story.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  6: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 7 | task=monthly_strategic | energy=1.00\n"
+      "  D 7: batch=3 rest=0 prompt_tok=1903\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2002\n",
-      "Day: Sun | days_elapsed=6\n",
-      "Energy: 1.00 | Followers: 10337\n",
-      "Engagement: 0.847 | Queue: 5\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=155\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"artistic expression\",\n",
-      "      \"tags\": [\"creativity\", \"art\", \"expression\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 17,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"famous artists\",\n",
-      "      \"tags\": [\"artists\", \"art\", \"celebrity\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Shares creative art through a reel and discovers famous artists.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  7: r=0.2134 e=1.00 posts=1 tools=0\n",
-      "  => grader=0.3456 reward=1.361\n",
-      "\n",
-      "  Task: monthly_competitive\n",
-      "\n",
-      ">>> Day 1 | task=monthly_competitive | energy=1.00\n"
+      "  D 8: batch=3 rest=0 prompt_tok=2151\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=538\n",
-      "Day: Mon | days_elapsed=0\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 0\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=152\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 7,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"daily routine\",\n",
-      "      \"tags\": [\"morning\", \"routine\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"weekly update\",\n",
-      "      \"tags\": [\"update\", \"news\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Created a daily reel focused on morning routines and a weekly update post promoting the news feed.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  1: r=0.2089 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 2 | task=monthly_competitive | energy=1.00\n"
+      "  D 9: batch=3 rest=0 prompt_tok=2256\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=778\n",
-      "Day: Tue | days_elapsed=1\n",
-      "Energy: 1.00 | Followers: 10042\n",
-      "Engagement: 0.426 | Queue: 0\n",
-      "Signals: watch=0.021 sends=0.068 saves=0.128\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=228\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"top trends\",\n",
-      "      \"tags\": [\"trending\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 11,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"travel tips\",\n",
-      "      \"tags\": [\"travel\", \"tips\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 13,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"day off\",\n",
-      "      \"tags\": [\"vacation\", \"relax\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Created stories about top trending topics, travel tips, and a day off reel focusing on relaxation and vacation. The high engagement from these posts indicates strong alignment with the niche and potential follower growth.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  2: r=0.2031 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 3 | task=monthly_competitive | energy=1.00\n"
+      "  D10: batch=3 rest=0 prompt_tok=2265\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1094\n",
-      "Day: Wed | days_elapsed=2\n",
-      "Energy: 1.00 | Followers: 10247\n",
-      "Engagement: 1.240 | Queue: 1\n",
-      "Signals: watch=1.027 sends=0.821 saves=0.205\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=280\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 1,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"best practices\",\n",
-      "      \"tags\": [\"success\", \"tips\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 3,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"product launches\",\n",
-      "      \"tags\": [\"launch\", \"exclusive\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 5,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"new album\",\n",
-      "      \"tags\": [\"music\", \"album\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 17,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"quarterly report\",\n",
-      "      \"tags\": [\"report\", \"analysis\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Created stories highlighting best practices and product launches as well as a quarter report post to promote new music releases. Posts aligned with interests show significant engagement, increasing the chance for future collaborations.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  3: r=0.2283 e=1.00 posts=2 tools=0\n",
-      "\n",
-      ">>> Day 4 | task=monthly_competitive | energy=1.00\n"
+      "  D11: batch=3 rest=0 prompt_tok=2263\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1462\n",
-      "Day: Thu | days_elapsed=3\n",
-      "Energy: 1.00 | Followers: 10318\n",
-      "Engagement: 0.800 | Queue: 1\n",
-      "Signals: watch=0.050 sends=0.128 saves=0.202\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=287\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 2,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"latest tech advancements\",\n",
-      "      \"tags\": [\"tech\", \"innovations\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 4,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"cooking tips\",\n",
-      "      \"tags\": [\"cooking\", \"tips\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"work-from-home tips\",\n",
-      "      \"tags\": [\"work-at-home\", \"tips\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 19,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"summer adventures\",\n",
-      "      \"tags\": [\"adventures\", \"summer\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Created stories on latest technology developments and cooking tips; posted work-from-home tips and summer adventure reels to maximize engagement. Continued to align with niche interests for consistent growth.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  4: r=0.2357 e=1.00 posts=2 tools=0\n",
-      "\n",
-      ">>> Day 5 | task=monthly_competitive | energy=1.00\n"
+      "  D12: batch=3 rest=0 prompt_tok=2274\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1837\n",
-      "Day: Fri | days_elapsed=4\n",
-      "Energy: 1.00 | Followers: 10509\n",
-      "Engagement: 0.853 | Queue: 1\n",
-      "Signals: watch=1.388 sends=0.517 saves=0.187\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=289\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 3,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"virtual reality experiences\",\n",
-      "      \"tags\": [\"vr\", \"experiences\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 5,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"local events\",\n",
-      "      \"tags\": [\"events\", \"local\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 7,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"holiday season\",\n",
-      "      \"tags\": [\"holidays\", \"seasonal\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 16,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"self-care during lockdown\",\n",
-      "      \"tags\": [\"self-care\", \"lockdown\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Created a VR experience reel, holiday-themed story, and self-care during lock-down reel. Added local event and seasonal stories to diversify content types for maximum engagement and potential future collaborations.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  5: r=0.2101 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 6 | task=monthly_competitive | energy=1.00\n"
+      "  D13: batch=3 rest=0 prompt_tok=2280\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2214\n",
-      "Day: Sat | days_elapsed=5\n",
-      "Energy: 1.00 | Followers: 10520\n",
-      "Engagement: 0.748 | Queue: 3\n",
-      "Signals: watch=0.023 sends=0.075 saves=0.006\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=292\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"office productivity hacks\",\n",
-      "      \"tags\": [\"hacks\", \"office\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 10,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"book recommendations\",\n",
-      "      \"tags\": [\"books\", \"recommendations\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 12,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"personal development\",\n",
-      "      \"tags\": [\"development\", \"life\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 14,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"mental health awareness\",\n",
-      "      \"tags\": [\"awareness\", \"health\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Created office productivity hack reel, book recommendations reel, personal development story, and mental health awareness story. Diversified content types to increase relevance and appeal, aiming to boost engagement while staying relevant to current interests.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  6: r=0.2128 e=1.00 posts=2 tools=0\n",
-      "\n",
-      ">>> Day 7 | task=monthly_competitive | energy=1.00\n"
+      "  D14: batch=3 rest=0 prompt_tok=2285\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2594\n",
-      "Day: Sun | days_elapsed=6\n",
-      "Energy: 1.00 | Followers: 10594\n",
-      "Engagement: 0.665 | Queue: 3\n",
-      "Signals: watch=0.350 sends=0.322 saves=0.071\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=275\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 2,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"remote learning resources\",\n",
-      "      \"tags\": [\"resources\", \"learning\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 5,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"career advancement\",\n",
-      "      \"tags\": [\"advancement\", \"career\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 7,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"environmental impact\",\n",
-      "      \"tags\": [\"impact\", \"environment\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 11,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"green living\",\n",
-      "      \"tags\": [\"living\", \"sustainable\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Created remote learning resource reel, career advancement story, and green living story to cater to diverse niches, maximizing engagement through varied content types.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  7: r=0.2247 e=1.00 posts=2 tools=0\n",
-      "  => grader=0.4808 reward=1.524\n",
+      "  D15: batch=3 rest=0 prompt_tok=2284\n",
       "\n",
       "============================================================\n",
-      "BEFORE TRAINING:\n",
-      "  monthly_engage: grader=0.3048\n",
-      "  monthly_strategic: grader=0.3456\n",
-      "  monthly_competitive: grader=0.4808\n"
+      "BEFORE TRAINING (took 84.9s):\n",
+      "  monthly_engage: grader=0.5642\n",
+      "  monthly_strategic: grader=0.5903\n",
+      "  monthly_competitive: grader=0.8313\n"
      ]
     }
    ],
    "source": [
-    "# Cell 9: Run untrained model\n",
-    "print(\"Running UNTRAINED base model on all tasks...\")\n",
+    "# Cell 9: Run untrained model (batched: all 3 tasks in parallel envs)\n",
+    "print(\"Running UNTRAINED base model on all tasks (batched)...\")\n",
     "print(\"=\" * 60)\n",
     "\n",
-    "before_results = {}\n",
-    "for task in TASKS:\n",
-    "    print(f\"\\n  Task: {task}\")\n",
-    "    result = run_llm_episode(model, tokenizer, task, seed=42, verbose=True)\n",
-    "    before_results[task] = result\n",
-    "    print(f\"  => grader={result['grader_score']:.4f} reward={result['total_reward']:.3f}\")\n",
+    "t0 = time.time()\n",
+    "results = run_llm_episodes_batched(model, tokenizer, [(t, 42) for t in TASKS], verbose=True)\n",
+    "before_results = {r[\"task\"]: r for r in results}\n",
     "\n",
     "print(\"\\n\" + \"=\" * 60)\n",
-    "print(\"BEFORE TRAINING:\")\n",
+    "print(f\"BEFORE TRAINING (took {time.time()-t0:.1f}s):\")\n",
     "for t in TASKS:\n",
     "    print(f\"  {t}: grader={before_results[t]['grader_score']:.4f}\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "85cbf989",
+   "id": "cedbfa6c",
    "metadata": {
     "papermill": {
-     "duration": 0.005544,
-     "end_time": "2026-04-25T22:51:33.788042+00:00",
+     "duration": 0.005613,
+     "end_time": "2026-04-26T01:01:27.854004+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:51:33.782498+00:00",
+     "start_time": "2026-04-26T01:01:27.848391+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2087,19 +1318,19 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "7ec70840",
+   "id": "5a72b090",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:51:33.798941Z",
-     "iopub.status.busy": "2026-04-25T22:51:33.798756Z",
-     "iopub.status.idle": "2026-04-25T22:51:34.172901Z",
-     "shell.execute_reply": "2026-04-25T22:51:34.171989Z"
+     "iopub.execute_input": "2026-04-26T01:01:27.864425Z",
+     "iopub.status.busy": "2026-04-26T01:01:27.864222Z",
+     "iopub.status.idle": "2026-04-26T01:01:28.551512Z",
+     "shell.execute_reply": "2026-04-26T01:01:28.550716Z"
     },
     "papermill": {
-     "duration": 0.380514,
-     "end_time": "2026-04-25T22:51:34.173548+00:00",
+     "duration": 0.693199,
+     "end_time": "2026-04-26T01:01:28.552016+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:51:33.793034+00:00",
+     "start_time": "2026-04-26T01:01:27.858817+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2109,7 +1340,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "trainable params: 18,464,768 || all params: 1,562,179,072 || trainable%: 1.1820\n"
+      "trainable params: 29,933,568 || all params: 3,115,872,256 || trainable%: 0.9607\n"
      ]
     }
    ],
@@ -2132,19 +1363,19 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "77c279af",
+   "id": "a918253b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T22:51:34.186355Z",
-     "iopub.status.busy": "2026-04-25T22:51:34.186156Z",
-     "iopub.status.idle": "2026-04-25T22:54:47.090532Z",
-     "shell.execute_reply": "2026-04-25T22:54:47.089375Z"
+     "iopub.execute_input": "2026-04-26T01:01:28.562801Z",
+     "iopub.status.busy": "2026-04-26T01:01:28.562588Z",
+     "iopub.status.idle": "2026-04-26T01:22:58.472018Z",
+     "shell.execute_reply": "2026-04-26T01:22:58.471114Z"
     },
     "papermill": {
-     "duration": 192.911985,
-     "end_time": "2026-04-25T22:54:47.091289+00:00",
+     "duration": 1289.91551,
+     "end_time": "2026-04-26T01:22:58.472721+00:00",
      "exception": false,
-     "start_time": "2026-04-25T22:51:34.179304+00:00",
+     "start_time": "2026-04-26T01:01:28.557211+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2156,385 +1387,143 @@
      "text": [
       "\n",
       "============================================================\n",
-      "TRAINING ROUND 1/1\n",
-      "============================================================\n",
-      "\n",
-      ">>> Day 1 | task=monthly_engage | energy=1.00\n"
+      "TRAINING ROUND 1/4\n",
+      "============================================================\n"
      ]
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=538\n",
-      "Day: Mon | days_elapsed=0\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 0\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=112\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"Mosseri\",\n",
-      "      \"tags\": [\"#Mosseri\", \"#Tech\", \"#Engineering\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Started planning for engagement on Monday by creating a reel related to 'Mosseri'. Focuses on sending bait.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "\n",
-      ">>> Day 2 | task=monthly_engage | energy=1.00\n"
+      "[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=738\n",
-      "Day: Tue | days_elapsed=1\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 1\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=343\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": {\n",
-      "        \"scheduled_actions\": [\n",
-      "          {\n",
-      "            \"hour\": 9,\n",
-      "            \"action_type\": \"post\",\n",
-      "            \"content_type\": \"story\",\n",
-      "            \"topic\": \"Mosseri\",\n",
-      "            \"tags\": [\"#Mosseri\", \"#Tech\", \"#Engineering\"],\n",
-      "            \"intent\": \"send_bait\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"hour\": 17,\n",
-      "            \"action_type\": \"create_content\",\n",
-      "            \"content_type\": \"reel\",\n",
-      "            \"topic\": \"Mosseri's latest project\",\n",
-      "            \"tags\": [\"#MosseriProject\", \"#Engineering\", \"#Innovation\"],\n",
-      "            \"intent\": \"save_bait\"\n",
-      "          }\n",
-      "        ]\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 0,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"Mosseri's daily update\",\n",
-      "      \"tags\": [\"#MosseriUpdate\", \"#TechNews\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"Top Mosseri projects\",\n",
-      "      \"tags\": [\"#MosseriProjects\", \"#Innovation\", \"#Technology\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Continued engagement efforts on Tuesday with stories and reels focusing on different intents but all related to 'Mosseri' for maximum reach.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "\n",
-      ">>> Day 3 | task=monthly_engage | energy=1.00\n"
+      "  Rollouts: 6 eps × 15 days in 299.0s\n",
+      "  ep 1/6:      engage grader=0.5430 reward=4.047 kept=15/15\n",
+      "  ep 2/6:   strategic grader=0.3790 reward=3.295 kept=15/15\n",
+      "  ep 3/6: competitive grader=0.0332 reward=2.316 kept=15/15\n",
+      "  ep 4/6:      engage grader=0.6470 reward=4.348 kept=15/15\n",
+      "  ep 5/6:   strategic grader=0.1750 reward=2.600 kept=1/15\n",
+      "  ep 6/6: competitive grader=0.0332 reward=2.316 kept=15/15\n",
+      "  Avg reward=3.154 Avg grader=0.3017 | total pairs=76\n",
+      "  Filtered to 38/76 samples (return >= 2.370)\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1183\n",
-      "Day: Wed | days_elapsed=2\n",
-      "Energy: 1.00 | Followers: 10005\n",
-      "Engagement: 0.059 | Queue: 1\n",
-      "Signals: watch=0.018 sends=0.023 saves=0.003\n",
-      "Tool results:\n",
-      "  predict_engagement: {\"predicted_daily_engagement\": 0.045}\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=343\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": {\n",
-      "        \"scheduled_actions\": [\n",
-      "          {\n",
-      "            \"hour\": 9,\n",
-      "            \"action_type\": \"post\",\n",
-      "            \"content_type\": \"story\",\n",
-      "            \"topic\": \"Latest Tech News\",\n",
-      "            \"tags\": [\"#TechNews\", \"#Mosseri\", \"#Engineering\"],\n",
-      "            \"intent\": \"watch_bait\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"hour\": 17,\n",
-      "            \"action_type\": \"create_content\",\n",
-      "            \"content_type\": \"reel\",\n",
-      "            \"topic\": \"Mosseri's future projects\",\n",
-      "            \"tags\": [\"#MosseriFuture\", \"#Innovation\", \"#Technology\"],\n",
-      "            \"intent\": \"like_bait\"\n",
-      "          }\n",
-      "        ]\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 0,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"Mosseri's Latest Update\",\n",
-      "      \"tags\": [\"#MosseriUpdate\", \"#Tech\", \"#Engineering\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"The Future of Technology in #Mosseri\",\n",
-      "      \"tags\": [\"#MosseriFuture\", \"#Innovation\", \"#Technology\"],\n",
-      "      \"intent\": \"save_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Projected engagement increases based on historical data. Continued focus on storytelling and innovation-related content targeting various intents.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "\n",
-      ">>> Day 4 | task=monthly_engage | energy=1.00\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ccd04af6d4f4a548bdb9f97e85ad6d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Adding EOS to train dataset:   0%|          | 0/38 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4ccd21c137e4c5f87b908a17752a7df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tokenizing train dataset:   0%|          | 0/38 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1628\n",
-      "Day: Thu | days_elapsed=3\n",
-      "Energy: 1.00 | Followers: 10010\n",
-      "Engagement: 0.059 | Queue: 1\n",
-      "Signals: watch=0.012 sends=0.037 saves=0.003\n",
-      "Tool results:\n",
-      "  predict_engagement: {\"predicted_daily_engagement\": 0.045}\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=353\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": {\n",
-      "        \"scheduled_actions\": [\n",
-      "          {\n",
-      "            \"hour\": 9,\n",
-      "            \"action_type\": \"post\",\n",
-      "            \"content_type\": \"story\",\n",
-      "            \"topic\": \"Interview with #Mosseri Founder\",\n",
-      "            \"tags\": [\"#InterviewWithMosseri\", \"#Leadership\", \"#Engineering\"],\n",
-      "            \"intent\": \"send_bait\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"hour\": 17,\n",
-      "            \"action_type\": \"create_content\",\n",
-      "            \"content_type\": \"reel\",\n",
-      "            \"topic\": \"Mosseri's Journey Through Innovation\",\n",
-      "            \"tags\": [\"#MosseriJourney\", \"#Innovation\", \"#Engineering\"],\n",
-      "            \"intent\": \"save_bait\"\n",
-      "          }\n",
-      "        ]\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 0,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"Mosseri's Impact on #TechIndustry\",\n",
-      "      \"tags\": [\"#MosseriImpact\", \"#TechIndustry\", \"#Engineering\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"Mosseri's Role in #Innovation\",\n",
-      "      \"tags\": [\"#MosseriRole\", \"#Innovation\", \"#Engineering\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Further optimization of strategies with more tailored content types targeting different intents as predicted by the model.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "\n",
-      ">>> Day 5 | task=monthly_engage | energy=1.00\n"
+      "[transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2083\n",
-      "Day: Fri | days_elapsed=4\n",
-      "Energy: 1.00 | Followers: 10015\n",
-      "Engagement: 0.059 | Queue: 1\n",
-      "Signals: watch=0.018 sends=0.023 saves=0.003\n",
-      "Tool results:\n",
-      "  predict_engagement: {\"predicted_daily_engagement\": 0.045}\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=364\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": {\n",
-      "        \"scheduled_actions\": [\n",
-      "          {\n",
-      "            \"hour\": 9,\n",
-      "            \"action_type\": \"post\",\n",
-      "            \"content_type\": \"story\",\n",
-      "            \"topic\": \"Mosseri's Vision for the #TechFuture\",\n",
-      "            \"tags\": [\"#TechVision\", \"#Mosseri\", \"#Innovation\"],\n",
-      "            \"intent\": \"send_bait\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"hour\": 17,\n",
-      "            \"action_type\": \"create_content\",\n",
-      "            \"content_type\": \"reel\",\n",
-      "            \"topic\": \"Mosseri's Innovations in #ArtificialIntelligence\",\n",
-      "            \"tags\": [\"#AIInnovation\", \"#Mosseri\", \"#Engineering\"],\n",
-      "            \"intent\": \"save_bait\"\n",
-      "          }\n",
-      "        ]\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 0,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"Mosseri's Contribution to #Cybersecurity\",\n",
-      "      \"tags\": [\"#Cybersecurity\", \"#Mosseri\", \"#Engineering\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"Mosseri's Leadership in #Sustainability\",\n",
-      "      \"tags\": [\"#SustainabilityLeadership\", \"#Mosseri\", \"#Engineering\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Final adjustments made to maximize engagement through a combination of storytelling and educational content types tailored to each intended outcome.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "\n",
-      ">>> Day 6 | task=monthly_engage | energy=1.00\n"
-     ]
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [10/10 00:14, Epoch 2/2]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2.771169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.820134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2.698111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.856578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2.780493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2.724246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>2.580899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>2.599474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>2.594157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>2.467900</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2549\n",
-      "Day: Sat | days_elapsed=5\n",
-      "Energy: 1.00 | Followers: 10019\n",
-      "Engagement: 0.056 | Queue: 1\n",
-      "Signals: watch=0.014 sends=0.019 saves=0.002\n",
-      "Tool results:\n",
-      "  predict_engagement: {\"predicted_daily_engagement\": 0.045}\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=353\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": {\n",
-      "        \"scheduled_actions\": [\n",
-      "          {\n",
-      "            \"hour\": 9,\n",
-      "            \"action_type\": \"post\",\n",
-      "            \"content_type\": \"story\",\n",
-      "            \"topic\": \"Interview with #Mosseri's Team Members\",\n",
-      "            \"tags\": [\"#TeamMembers\", \"#Mosseri\", \"#Innovation\"],\n",
-      "            \"intent\": \"send_bait\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"hour\": 17,\n",
-      "            \"action_type\": \"create_content\",\n",
-      "            \"content_type\": \"reel\",\n",
-      "            \"topic\": \"Mosseri's Role in #Education\",\n",
-      "            \"tags\": [\"#EducationalInnovation\", \"#Mosseri\", \"#Engineering\"],\n",
-      "            \"intent\": \"save_bait\"\n",
-      "          }\n",
-      "        ]\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 0,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"The Importance of #WomenInSTEM\",\n",
-      "      \"tags\": [\"#WomenInSTEM\", \"#Education\", \"#Mosseri\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"Mosseri's Work in #SocialMedia\",\n",
-      "      \"tags\": [\"#SocialMediaWork\", \"#Mosseri\", \"#Innovation\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Final checks show continued engagement improvements with targeted stories and reels enhancing reach across various interests.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
+      "  Training loss: 2.6893\n",
       "\n",
-      ">>> Day 7 | task=monthly_engage | energy=1.00\n"
+      "============================================================\n",
+      "TRAINING ROUND 2/4\n",
+      "============================================================\n"
      ]
     },
     {
@@ -2548,80 +1537,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=3004\n",
-      "Day: Sun | days_elapsed=6\n",
-      "Energy: 1.00 | Followers: 10024\n",
-      "Engagement: 0.056 | Queue: 1\n",
-      "Signals: watch=0.018 sends=0.023 saves=0.003\n",
-      "Tool results:\n",
-      "  predict_engagement: {\"predicted_daily_engagement\": 0.045}\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=356\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": {\n",
-      "        \"scheduled_actions\": [\n",
-      "          {\n",
-      "            \"hour\": 9,\n",
-      "            \"action_type\": \"post\",\n",
-      "            \"content_type\": \"story\",\n",
-      "            \"topic\": \"Interview with #Mosseri's Team Members\",\n",
-      "            \"tags\": [\"#TeamMembers\", \"#Mosseri\", \"#Innovation\"],\n",
-      "            \"intent\": \"send_bait\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"hour\": 17,\n",
-      "            \"action_type\": \"create_content\",\n",
-      "            \"content_type\": \"reel\",\n",
-      "            \"topic\": \"Mosseri's Role in #Cybersecurity\",\n",
-      "            \"tags\": [\"#Cybersecurity\", \"#Mosseri\", \"#Engineering\"],\n",
-      "            \"intent\": \"save_bait\"\n",
-      "          }\n",
-      "        ]\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 0,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"The Importance of #WomenInSTEM\",\n",
-      "      \"tags\": [\"#WomenInSTEM\", \"#Education\", \"#Mosseri\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"Mosseri's Work in #SocialMedia\",\n",
-      "      \"tags\": [\"#SocialMediaWork\", \"#Mosseri\", \"#Innovation\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Final adjustments have been made to optimize engagement further with high-intent content types throughout the week.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "  ep 1/1:      engage grader=0.0268 reward=1.593 return[min=0.36 max=1.41 mean=0.94]\n",
-      "  Avg reward=1.593 Avg grader=0.0268\n",
-      "  Filtered to 4/7 samples (return >= 0.968)\n"
+      "  Rollouts: 6 eps × 15 days in 430.7s\n",
+      "  ep 1/6:      engage grader=0.5125 reward=3.710 kept=14/15\n",
+      "  ep 2/6:   strategic grader=0.1747 reward=2.599 kept=14/15\n",
+      "  ep 3/6: competitive grader=0.2412 reward=3.346 kept=15/15\n",
+      "  ep 4/6:      engage grader=0.0000 reward=2.249 kept=15/15\n",
+      "  ep 5/6:   strategic grader=0.1750 reward=2.600 kept=1/15\n",
+      "  ep 6/6: competitive grader=0.1312 reward=2.623 kept=15/15\n",
+      "  Avg reward=2.855 Avg grader=0.2058 | total pairs=74\n",
+      "  Filtered to 37/74 samples (return >= 1.711)\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6e2e90c8df324fcb8ceb6b633d1ceb65",
+       "model_id": "c23456867d644af5bfc9d822b97964c5",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Adding EOS to train dataset:   0%|          | 0/4 [00:00<?, ? examples/s]"
+       "Adding EOS to train dataset:   0%|          | 0/37 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
@@ -2630,32 +1565,25 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ace8e79cc1234b8abe7a85973a70dcf4",
+       "model_id": "209b910e79894ecd8b415071b8e5643a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Tokenizing train dataset:   0%|          | 0/4 [00:00<?, ? examples/s]"
+       "Tokenizing train dataset:   0%|          | 0/37 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
        "\n",
        "    <div>\n",
        "      \n",
-       "      <progress value='7' max='7' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [7/7 00:03, Epoch 7/7]\n",
+       "      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [10/10 00:12, Epoch 2/2]\n",
        "    </div>\n",
        "    <table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -2667,31 +1595,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>2.368976</td>\n",
+       "      <td>2.730174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>2.368976</td>\n",
+       "      <td>2.675708</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
-       "      <td>2.356734</td>\n",
+       "      <td>2.682244</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
-       "      <td>2.335494</td>\n",
+       "      <td>2.612569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>5</td>\n",
-       "      <td>2.314747</td>\n",
+       "      <td>2.581834</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>6</td>\n",
-       "      <td>2.294741</td>\n",
+       "      <td>2.519363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>7</td>\n",
-       "      <td>2.279922</td>\n",
+       "      <td>2.534280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>2.428933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>2.416718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>2.358385</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>"
@@ -2707,1487 +1647,570 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Training loss: 2.3314\n",
-      "\n",
-      "Training complete in 3.2 min\n",
-      " round  avg_episode_reward  max_episode_reward  min_episode_reward  avg_grader  max_grader  n_training_samples  train_loss\n",
-      "     1               1.593               1.593               1.593      0.0268      0.0268                   4      2.3314\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Cell 11: Training loop\n",
-    "from trl import SFTTrainer, SFTConfig\n",
-    "from datasets import Dataset\n",
-    "\n",
-    "NUM_ROUNDS = 1\n",
-    "EPISODES_PER_ROUND = 1\n",
-    "TOP_K_FRACTION = 0.5\n",
-    "\n",
-    "training_log = {\n",
-    "    \"round\": [], \"avg_episode_reward\": [], \"max_episode_reward\": [],\n",
-    "    \"min_episode_reward\": [], \"avg_grader\": [], \"max_grader\": [],\n",
-    "    \"n_training_samples\": [], \"train_loss\": [],\n",
-    "}\n",
-    "\n",
-    "t_start = time.time()\n",
-    "\n",
-    "for round_idx in range(1, NUM_ROUNDS + 1):\n",
-    "    print(f\"\\n{'=' * 60}\")\n",
-    "    print(f\"TRAINING ROUND {round_idx}/{NUM_ROUNDS}\")\n",
-    "    print(f\"{'=' * 60}\")\n",
-    "\n",
-    "    # Collect episodes\n",
-    "    peft_model.eval()\n",
-    "    all_pairs, episode_rewards, episode_graders = [], [], []\n",
-    "\n",
-    "    for ep in range(EPISODES_PER_ROUND):\n",
-    "        task = TASKS[ep % len(TASKS)]\n",
-    "        seed = 42 + (round_idx - 1) * 100 + ep\n",
-    "        result = run_llm_episode(peft_model, tokenizer, task, seed=seed)\n",
-    "        ep_reward = result[\"total_reward\"] + 2.0 * result[\"grader_score\"]\n",
-    "        episode_rewards.append(ep_reward)\n",
-    "        episode_graders.append(result[\"grader_score\"])\n",
-    "\n",
-    "        for pr in result[\"pairs\"]:\n",
-    "            text = (f\"<|im_start|>system\\n{SYSTEM_PROMPT}<|im_end|>\\n\"\n",
-    "                    f\"<|im_start|>user\\n{pr['prompt']}<|im_end|>\\n\"\n",
-    "                    f\"<|im_start|>assistant\\n{pr['response']}<|im_end|>\")\n",
-    "            all_pairs.append({\"text\": text, \"reward\": pr[\"return\"]})\n",
-    "\n",
-    "        rets = result[\"returns\"]\n",
-    "        print(f\"  ep {ep+1}/{EPISODES_PER_ROUND}: {task.split('_')[-1]:>11s} \"\n",
-    "              f\"grader={result['grader_score']:.4f} reward={ep_reward:.3f} \"\n",
-    "              f\"return[min={min(rets):.2f} max={max(rets):.2f} mean={np.mean(rets):.2f}]\")\n",
-    "\n",
-    "    avg_r = np.mean(episode_rewards)\n",
-    "    avg_g = np.mean(episode_graders)\n",
-    "    print(f\"  Avg reward={avg_r:.3f} Avg grader={avg_g:.4f}\")\n",
-    "\n",
-    "    # Filter to top-K by per-pair return (per-step credit assignment)\n",
-    "    threshold = np.percentile([p[\"reward\"] for p in all_pairs], (1 - TOP_K_FRACTION) * 100)\n",
-    "    filtered = [p for p in all_pairs if p[\"reward\"] >= threshold] or all_pairs\n",
-    "    print(f\"  Filtered to {len(filtered)}/{len(all_pairs)} samples (return >= {threshold:.3f})\")\n",
-    "\n",
-    "    dataset = Dataset.from_list([{\"text\": p[\"text\"]} for p in filtered])\n",
-    "\n",
-    "    # SFT training (real gradient updates)\n",
-    "    sft_config = SFTConfig(\n",
-    "        output_dir=f\"./checkpoints/round_{round_idx}\",\n",
-    "        max_steps=7,\n",
-    "        per_device_train_batch_size=32,\n",
-    "        gradient_accumulation_steps=1,\n",
-    "        learning_rate=2e-5,\n",
-    "        warmup_ratio=0.1,\n",
-    "        logging_steps=1,\n",
-    "        save_strategy=\"no\",\n",
-    "        max_length=1024,\n",
-    "        bf16=True,\n",
-    "        gradient_checkpointing=False,\n",
-    "        dataloader_num_workers=4,\n",
-    "        dataloader_pin_memory=True,\n",
-    "        optim=\"adamw_torch_fused\",\n",
-    "        report_to=\"none\",\n",
-    "    )\n",
-    "\n",
-    "    peft_model.train()\n",
-    "    trainer = SFTTrainer(\n",
-    "        model=peft_model, processing_class=tokenizer,\n",
-    "        train_dataset=dataset, args=sft_config,\n",
-    "    )\n",
-    "    train_result = trainer.train()\n",
-    "    loss = train_result.training_loss\n",
-    "    print(f\"  Training loss: {loss:.4f}\")\n",
-    "\n",
-    "    training_log[\"round\"].append(round_idx)\n",
-    "    training_log[\"avg_episode_reward\"].append(round(float(avg_r), 3))\n",
-    "    training_log[\"max_episode_reward\"].append(round(float(max(episode_rewards)), 3))\n",
-    "    training_log[\"min_episode_reward\"].append(round(float(min(episode_rewards)), 3))\n",
-    "    training_log[\"avg_grader\"].append(round(float(avg_g), 4))\n",
-    "    training_log[\"max_grader\"].append(round(float(max(episode_graders)), 4))\n",
-    "    training_log[\"n_training_samples\"].append(len(filtered))\n",
-    "    training_log[\"train_loss\"].append(round(loss, 4))\n",
-    "\n",
-    "elapsed = time.time() - t_start\n",
-    "print(f\"\\nTraining complete in {elapsed/60:.1f} min\")\n",
-    "print(pd.DataFrame(training_log).to_string(index=False))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b2d1f32",
-   "metadata": {
-    "papermill": {
-     "duration": 0.005971,
-     "end_time": "2026-04-25T22:54:47.103776+00:00",
-     "exception": false,
-     "start_time": "2026-04-25T22:54:47.097805+00:00",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "## Part 5: Trained LLM Evaluation (“After”)\n",
-    "\n",
-    "Same model, same seeds, same environment — but now with updated LoRA weights."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "8d7a70c2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-04-25T22:54:47.116891Z",
-     "iopub.status.busy": "2026-04-25T22:54:47.116625Z",
-     "iopub.status.idle": "2026-04-25T23:01:26.537356Z",
-     "shell.execute_reply": "2026-04-25T23:01:26.536404Z"
-    },
-    "papermill": {
-     "duration": 399.428137,
-     "end_time": "2026-04-25T23:01:26.538012+00:00",
-     "exception": false,
-     "start_time": "2026-04-25T22:54:47.109875+00:00",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running TRAINED model on all tasks...\n",
-      "============================================================\n",
+      "  Training loss: 2.5540\n",
       "\n",
-      "  Task: monthly_engage\n",
-      "\n",
-      ">>> Day 1 | task=monthly_engage | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=538\n",
-      "Day: Mon | days_elapsed=0\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 0\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=153\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"day_in_the_life\",\n",
-      "      \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 17,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"work_outside\",\n",
-      "      \"tags\": [\"exercise\", \"outdoors\", \"motivation\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Start building content and engagement plans.\"\n",
-      "}\n",
       "============================================================\n",
-      "    Day  1: r=0.1813 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 2 | task=monthly_engage | energy=1.00\n"
+      "TRAINING ROUND 3/4\n",
+      "============================================================\n"
      ]
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=779\n",
-      "Day: Tue | days_elapsed=1\n",
-      "Energy: 1.00 | Followers: 10020\n",
-      "Engagement: 0.205 | Queue: 0\n",
-      "Signals: watch=0.041 sends=0.082 saves=0.010\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=281\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"scheduled_actions\": [\n",
-      "            {\n",
-      "              \"hour\": 9,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"reel\",\n",
-      "              \"topic\": \"day_in_the_life\",\n",
-      "              \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "              \"intent\": \"watch_bait\"\n",
-      "            }\n",
-      "          ]\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"draft_review\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"scheduled_actions\": [\n",
-      "            {\n",
-      "              \"hour\": 9,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"reel\",\n",
-      "              \"topic\": \"day_in_the_life\",\n",
-      "              \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "              \"intent\": \"watch_bait\"\n",
-      "            }\n",
-      "          ]\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"day_in_the_life\",\n",
-      "      \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Review predictions and drafts before executing.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  2: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 3 | task=monthly_engage | energy=1.00\n"
+      "[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1148\n",
-      "Day: Wed | days_elapsed=2\n",
-      "Energy: 1.00 | Followers: 10020\n",
-      "Engagement: 0.205 | Queue: 0\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=250\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"scheduled_actions\": [\n",
-      "            {\n",
-      "              \"hour\": 9,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"reel\",\n",
-      "              \"topic\": \"day_in_the_life\",\n",
-      "              \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "              \"intent\": \"watch_bait\"\n",
-      "            },\n",
-      "            {\n",
-      "              \"hour\": 17,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"story\",\n",
-      "              \"topic\": \"work_outside\",\n",
-      "              \"tags\": [\"exercise\", \"outdoors\", \"motivation\"],\n",
-      "              \"intent\": \"like_bait\"\n",
-      "            }\n",
-      "          ]\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"day_in_the_life\",\n",
-      "      \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Execute predictions and reviews on daily basis.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  3: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 4 | task=monthly_engage | energy=1.00\n"
+      "  Rollouts: 6 eps × 15 days in 286.1s\n",
+      "  ep 1/6:      engage grader=0.0000 reward=2.250 kept=15/15\n",
+      "  ep 2/6:   strategic grader=0.3385 reward=3.293 kept=15/15\n",
+      "  ep 3/6: competitive grader=0.0332 reward=2.316 kept=15/15\n",
+      "  ep 4/6:      engage grader=0.2130 reward=3.219 kept=15/15\n",
+      "  ep 5/6:   strategic grader=0.1743 reward=2.599 kept=15/15\n",
+      "  ep 6/6: competitive grader=0.4656 reward=4.140 kept=15/15\n",
+      "  Avg reward=2.969 Avg grader=0.2041 | total pairs=90\n",
+      "  Filtered to 45/90 samples (return >= 1.742)\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1486\n",
-      "Day: Thu | days_elapsed=3\n",
-      "Energy: 1.00 | Followers: 10020\n",
-      "Engagement: 0.205 | Queue: 0\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=287\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"scheduled_actions\": [\n",
-      "            {\n",
-      "              \"hour\": 9,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"reel\",\n",
-      "              \"topic\": \"day_in_the_life\",\n",
-      "              \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "              \"intent\": \"watch_bait\"\n",
-      "            },\n",
-      "            {\n",
-      "              \"hour\": 17,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"story\",\n",
-      "              \"topic\": \"work_outside\",\n",
-      "              \"tags\": [\"exercise\", \"outdoors\", \"motivation\"],\n",
-      "              \"intent\": \"like_bait\"\n",
-      "            }\n",
-      "          ]\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"inspiration\"\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"day_in_the_life\",\n",
-      "      \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Analyze tag history for inspiration to enhance future content choices.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  4: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 5 | task=monthly_engage | energy=1.00\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55862e5d38bb45aabdbb5cdb2aa78dcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Adding EOS to train dataset:   0%|          | 0/45 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1860\n",
-      "Day: Fri | days_elapsed=4\n",
-      "Energy: 1.00 | Followers: 9678\n",
-      "Engagement: 0.205 | Queue: 0\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=284\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"scheduled_actions\": [\n",
-      "            {\n",
-      "              \"hour\": 9,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"reel\",\n",
-      "              \"topic\": \"day_in_the_life\",\n",
-      "              \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "              \"intent\": \"watch_bait\"\n",
-      "            },\n",
-      "            {\n",
-      "              \"hour\": 17,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"story\",\n",
-      "              \"topic\": \"work_outside\",\n",
-      "              \"tags\": [\"exercise\", \"outdoors\", \"motivation\"],\n",
-      "              \"intent\": \"like_bait\"\n",
-      "            }\n",
-      "          ]\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"inspiration\"\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"day_in_the_life\",\n",
-      "      \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Use predicted engagement data to refine content choices.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  5: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 6 | task=monthly_engage | energy=1.00\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bc6a332dbca4bef8696f1da49cb1ebf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tokenizing train dataset:   0%|          | 0/45 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2231\n",
-      "Day: Sat | days_elapsed=5\n",
-      "Energy: 1.00 | Followers: 8592\n",
-      "Engagement: 0.205 | Queue: 0\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=288\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"scheduled_actions\": [\n",
-      "            {\n",
-      "              \"hour\": 9,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"reel\",\n",
-      "              \"topic\": \"day_in_the_life\",\n",
-      "              \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "              \"intent\": \"watch_bait\"\n",
-      "            },\n",
-      "            {\n",
-      "              \"hour\": 17,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"story\",\n",
-      "              \"topic\": \"work_outside\",\n",
-      "              \"tags\": [\"exercise\", \"outdoors\", \"motivation\"],\n",
-      "              \"intent\": \"like_bait\"\n",
-      "            }\n",
-      "          ]\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"inspiration\"\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"day_in_the_life\",\n",
-      "      \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Continuously monitor and optimize based on evolving trends and user feedback.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  6: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 7 | task=monthly_engage | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2606\n",
-      "Day: Sun | days_elapsed=6\n",
-      "Energy: 1.00 | Followers: 7630\n",
-      "Engagement: 0.205 | Queue: 0\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=289\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"scheduled_actions\": [\n",
-      "            {\n",
-      "              \"hour\": 9,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"reel\",\n",
-      "              \"topic\": \"day_in_the_life\",\n",
-      "              \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "              \"intent\": \"watch_bait\"\n",
-      "            },\n",
-      "            {\n",
-      "              \"hour\": 17,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"story\",\n",
-      "              \"topic\": \"work_outside\",\n",
-      "              \"tags\": [\"exercise\", \"outdoors\", \"motivation\"],\n",
-      "              \"intent\": \"like_bait\"\n",
-      "            }\n",
-      "          ]\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"inspiration\"\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"day_in_the_life\",\n",
-      "      \"tags\": [\"daily\", \"morning\", \"inspiration\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Continue to refine your content strategies based on historical data and current trends.\"\n",
-      "}\n",
-      "============================================================\n",
-      "    Day  7: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "  => grader=0.0162 reward=1.081\n",
-      "\n",
-      "  Task: monthly_strategic\n",
-      "\n",
-      ">>> Day 1 | task=monthly_strategic | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=538\n",
-      "Day: Mon | days_elapsed=0\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 0\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=100\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Monday Motivation\",\n",
-      "      \"tags\": [\"monday\", \"motivation\", \"start\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Started the week by sharing Monday motivation and encouraging engagement.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  1: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 2 | task=monthly_strategic | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=725\n",
-      "Day: Tue | days_elapsed=1\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 1\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=296\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"scheduled_actions\": [\n",
-      "            {\n",
-      "              \"hour\": 6,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"text_post\",\n",
-      "              \"topic\": \"Mon Motivation\",\n",
-      "              \"tags\": [\"monday\", \"motivation\", \"start\"],\n",
-      "              \"intent\": \"like_bait\"\n",
-      "            }\n",
-      "          ]\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"draft_review\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"scheduled_actions\": [\n",
-      "            {\n",
-      "              \"hour\": 6,\n",
-      "              \"action_type\": \"create_content\",\n",
-      "              \"content_type\": \"text_post\",\n",
-      "              \"topic\": \"Mon Motivation\",\n",
-      "              \"tags\": [\"monday\", \"motivation\", \"start\"],\n",
-      "              \"intent\": \"like_bait\"\n",
-      "            }\n",
-      "          ]\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 7,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Mon Motivation\",\n",
-      "      \"tags\": [\"monday\", \"motivation\", \"start\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Reviewed and drafted the first post for the week. Sent it out on Tuesday morning to maximize engagement.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  2: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 3 | task=monthly_strategic | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1109\n",
-      "Day: Wed | days_elapsed=2\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 1\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=247\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"monday\"\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_audience\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"segment_id\": \"1\",\n",
-      "          \"window_days\": 7\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Motivation Monday\",\n",
-      "      \"tags\": [\"monday\", \"workout\", \"goals\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 14,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Workout Motivation\",\n",
-      "      \"tags\": [\"monday\", \"workout\", \"goals\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Reviewed audience preferences and crafted motivational posts for the upcoming weeks. Distributed them throughout the week to ensure consistent engagement.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  3: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 4 | task=monthly_strategic | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1443\n",
-      "Day: Thu | days_elapsed=3\n",
-      "Energy: 1.00 | Followers: 9950\n",
-      "Engagement: 0.000 | Queue: 1\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=279\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"monday\"\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"workout\"\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_audience\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"segment_id\": \"2\",\n",
-      "          \"window_days\": 7\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 9,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Motivation Monday\",\n",
-      "      \"tags\": [\"monday\", \"workout\", \"goals\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 15,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Workout Tips\",\n",
-      "      \"tags\": [\"monday\", \"workout\", \"tips\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Reviewed audience preferences again and created more specific motivational and workout-related posts. Ensured consistency across different segments and audiences.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  4: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 5 | task=monthly_strategic | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1809\n",
-      "Day: Fri | days_elapsed=4\n",
-      "Energy: 1.00 | Followers: 8833\n",
-      "Engagement: 0.000 | Queue: 1\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=279\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"monday\"\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"workout\"\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"friday\"\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 10,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Weekend Motivation\",\n",
-      "      \"tags\": [\"weekend\", \"motivation\", \"fun\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 16,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Weekly Workout Recap\",\n",
-      "      \"tags\": [\"weekend\", \"workout\", \"goals\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Reviewed all relevant tags and found that 'friday' was very popular among our audience. Created weekend-themed motivational and recap posts specifically tailored to Fridays.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  5: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 6 | task=monthly_strategic | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2175\n",
-      "Day: Sat | days_elapsed=5\n",
-      "Energy: 1.00 | Followers: 7843\n",
-      "Engagement: 0.000 | Queue: 1\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=241\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"weekend\"\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"friday\"\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 11,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Weekend Fun\",\n",
-      "      \"tags\": [\"weekend\", \"motivation\", \"activities\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 17,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Friday Fitness Goals\",\n",
-      "      \"tags\": [\"friday\", \"fitness\", \"goals\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Continued reviewing and creating posts based on popular weekends and Friday themes. Ensured variety within each theme to keep the audience engaged.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  6: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "\n",
-      ">>> Day 7 | task=monthly_strategic | energy=1.00\n"
-     ]
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='12' max='12' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [12/12 00:17, Epoch 2/2]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2.225861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.170919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2.239033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.092458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2.078332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2.095270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>2.075058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>2.021818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>1.998803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>1.992182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>1.922094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>1.996073</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2503\n",
-      "Day: Sun | days_elapsed=6\n",
-      "Energy: 1.00 | Followers: 6965\n",
-      "Engagement: 0.000 | Queue: 1\n",
-      "Signals: watch=0.000 sends=0.000 saves=0.000\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=345\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"weekend\"\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"friday\"\n",
-      "        }\n",
-      "      ]\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": [\n",
-      "        {\n",
-      "          \"tag\": \"sunrise\"\n",
-      "        }\n",
-      "      ]\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 12,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Endless Weekend Activities\",\n",
-      "      \"tags\": [\"weekend\", \"motivation\", \"activities\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 18,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Friday Morning Routine\",\n",
-      "      \"tags\": [\"friday\", \"morning\", \"routine\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 22,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"Sunrise Routine\",\n",
-      "      \"tags\": [\"sunrise\", \"morning\", \"routines\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Reviewed all popular weekend and Friday themes. Created multiple posts related to end-of-week activities, morning routines, and sunrise routines to cater to various interests and time slots.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  7: r=0.1500 e=1.00 posts=0 tools=0\n",
-      "  => grader=0.1749 reward=1.050\n",
-      "\n",
-      "  Task: monthly_competitive\n",
+      "  Training loss: 2.0757\n",
       "\n",
-      ">>> Day 1 | task=monthly_competitive | energy=1.00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=538\n",
-      "Day: Mon | days_elapsed=0\n",
-      "Energy: 1.00 | Followers: 10000\n",
-      "Engagement: 0.000 | Queue: 0\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=90\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"daily routine\",\n",
-      "      \"tags\": [\"morning\", \"routine\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Started with a focus on engaging early mornings.\"\n",
-      "}\n",
-      "```\n",
       "============================================================\n",
-      "    Day  1: r=0.2131 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 2 | task=monthly_competitive | energy=1.00\n"
+      "TRAINING ROUND 4/4\n",
+      "============================================================\n"
      ]
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=716\n",
-      "Day: Tue | days_elapsed=1\n",
-      "Energy: 1.00 | Followers: 10009\n",
-      "Engagement: 0.098 | Queue: 0\n",
-      "Signals: watch=0.020 sends=0.039 saves=0.005\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=149\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 7,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"text_post\",\n",
-      "      \"topic\": \"new recipe\",\n",
-      "      \"tags\": [\"food\", \"recipes\"],\n",
-      "      \"intent\": \"send_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"workout tips\",\n",
-      "      \"tags\": [\"fitness\", \"exercise\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Increased engagement through new content types and optimized bait strategies.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  2: r=0.2151 e=1.00 posts=1 tools=0\n",
-      "\n",
-      ">>> Day 3 | task=monthly_competitive | energy=1.00\n"
+      "[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=953\n",
-      "Day: Wed | days_elapsed=2\n",
-      "Energy: 1.00 | Followers: 10049\n",
-      "Engagement: 0.254 | Queue: 1\n",
-      "Signals: watch=0.020 sends=0.066 saves=0.123\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=291\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"predict_engagement\",\n",
-      "      \"arguments\": {\n",
-      "        \"scheduled_actions\": [\n",
-      "          {\n",
-      "            \"hour\": 7,\n",
-      "            \"action_type\": \"post\",\n",
-      "            \"content_type\": \"text_post\",\n",
-      "            \"topic\": \"new recipe\",\n",
-      "            \"tags\": [\"food\", \"recipes\"],\n",
-      "            \"intent\": \"send_bait\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"hour\": 8,\n",
-      "            \"action_type\": \"create_content\",\n",
-      "            \"content_type\": \"reel\",\n",
-      "            \"topic\": \"workout tips\",\n",
-      "            \"tags\": [\"fitness\", \"exercise\"],\n",
-      "            \"intent\": \"watch_bait\"\n",
-      "          }\n",
-      "        ]\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"story\",\n",
-      "      \"topic\": \"daily routine\",\n",
-      "      \"tags\": [\"morning\", \"routine\"],\n",
-      "      \"intent\": \"like_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"workout tips\",\n",
-      "      \"tags\": [\"fitness\", \"exercise\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Optimized post creation schedule based on predicted engagement levels.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  3: r=0.2130 e=1.00 posts=1 tools=1\n",
-      "\n",
-      ">>> Day 4 | task=monthly_competitive | energy=1.00\n"
+      "  Rollouts: 6 eps × 15 days in 199.4s\n",
+      "  ep 1/6:      engage grader=0.0793 reward=2.443 kept=15/15\n",
+      "  ep 2/6:   strategic grader=0.5243 reward=4.527 kept=15/15\n",
+      "  ep 3/6: competitive grader=0.0350 reward=2.320 kept=15/15\n",
+      "  ep 4/6:      engage grader=0.3129 reward=3.254 kept=15/15\n",
+      "  ep 5/6:   strategic grader=0.4373 reward=3.809 kept=15/15\n",
+      "  ep 6/6: competitive grader=0.2871 reward=3.410 kept=15/15\n",
+      "  Avg reward=3.294 Avg grader=0.2793 | total pairs=90\n",
+      "  Filtered to 45/90 samples (return >= 2.252)\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1347\n",
-      "Day: Thu | days_elapsed=3\n",
-      "Energy: 1.00 | Followers: 10057\n",
-      "Engagement: 0.198 | Queue: 1\n",
-      "Signals: watch=0.017 sends=0.034 saves=0.004\n",
-      "Tool results:\n",
-      "  predict_engagement: {\"predicted_daily_engagement\": 0.1229}\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=215\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": {\n",
-      "        \"tag\": \"daily_routine\"\n",
-      "      }\n",
-      "    },\n",
-      "    {\n",
-      "      \"name\": \"query_tag_history\",\n",
-      "      \"arguments\": {\n",
-      "        \"tag\": \"fitness_exercise\"\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"tips for morning routines\",\n",
-      "      \"tags\": [\"morning\", \"routines\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"best workout routines\",\n",
-      "      \"tags\": [\"fitness\", \"exercises\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Improved daily routines and workout content resonated well among users.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  4: r=0.2167 e=1.00 posts=1 tools=2\n",
-      "\n",
-      ">>> Day 5 | task=monthly_competitive | energy=1.00\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15712b7dc4e744cc926f528aada5a770",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Adding EOS to train dataset:   0%|          | 0/45 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=1696\n",
-      "Day: Fri | days_elapsed=4\n",
-      "Energy: 1.00 | Followers: 10123\n",
-      "Engagement: 0.316 | Queue: 1\n",
-      "Signals: watch=0.502 sends=0.167 saves=0.067\n",
-      "Tool results:\n",
-      "  query_tag_history: {\"tag\": \"daily_routine\", \"uses\": 0, \"avg_signals\": {}}\n",
-      "  query_tag_history: {\"tag\": \"fitness_exercise\", \"uses\": 0, \"avg_signals\": {}}\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=298\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"draft_review\",\n",
-      "      \"arguments\": {\n",
-      "        \"scheduled_actions\": [\n",
-      "          {\n",
-      "            \"hour\": 6,\n",
-      "            \"action_type\": \"post\",\n",
-      "            \"content_type\": \"reel\",\n",
-      "            \"topic\": \"tips for morning routines\",\n",
-      "            \"tags\": [\"morning\", \"routines\"],\n",
-      "            \"intent\": \"watch_bait\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"hour\": 8,\n",
-      "            \"action_type\": \"create_content\",\n",
-      "            \"content_type\": \"reel\",\n",
-      "            \"topic\": \"best workout routines\",\n",
-      "            \"tags\": [\"fitness\", \"exercises\"],\n",
-      "            \"intent\": \"watch_bait\"\n",
-      "          }\n",
-      "        ]\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"tips for morning routines\",\n",
-      "      \"tags\": [\"morning\", \"routines\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"best workout routines\",\n",
-      "      \"tags\": [\"fitness\", \"exercises\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Reviewing drafted content improved targeting and engagement.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  5: r=0.2173 e=1.00 posts=1 tools=1\n",
-      "\n",
-      ">>> Day 6 | task=monthly_competitive | energy=1.00\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de81e605c1744caa93f503dfa0c58bf7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tokenizing train dataset:   0%|          | 0/45 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2100\n",
-      "Day: Sat | days_elapsed=5\n",
-      "Energy: 1.00 | Followers: 10199\n",
-      "Engagement: 0.405 | Queue: 1\n",
-      "Signals: watch=0.570 sends=0.190 saves=0.076\n",
-      "Tool results:\n",
-      "  draft_review: {\"feedback\": [\"Good posting frequency for today.\"], \"post_count\": 1}\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=173\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"schedule_next_day\",\n",
-      "      \"arguments\": {}\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"tips for morning routines\",\n",
-      "      \"tags\": [\"morning\", \"routines\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"best workout routines\",\n",
-      "      \"tags\": [\"fitness\", \"exercises\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Scheduled next post and content creation for continued growth.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  6: r=0.2155 e=1.00 posts=1 tools=1\n",
-      "\n",
-      ">>> Day 7 | task=monthly_competitive | energy=1.00\n"
-     ]
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='12' max='12' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [12/12 00:15, Epoch 2/2]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2.101937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.069137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2.114893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.032242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2.023777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>1.962225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1.979426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>1.921424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>1.907600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>1.893878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>1.873059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>1.885975</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "============================================================\n",
-      "[LLM PROMPT] tokens=2361\n",
-      "Day: Sun | days_elapsed=6\n",
-      "Energy: 1.00 | Followers: 10247\n",
-      "Engagement: 0.418 | Queue: 1\n",
-      "Signals: watch=0.365 sends=0.122 saves=0.049\n",
-      "Tool results:\n",
-      "  (none)\n",
-      "Plan your actions (JSON only):\n",
-      "------------------------------------------------------------\n",
-      "[LLM RESPONSE] tokens=203\n",
-      "```json\n",
-      "{\n",
-      "  \"tool_calls\": [\n",
-      "    {\n",
-      "      \"name\": \"propose_collab\",\n",
-      "      \"arguments\": {\n",
-      "        \"partner_id\": 12345,\n",
-      "        \"content_type\": \"reel\",\n",
-      "        \"hour\": 6\n",
-      "      }\n",
-      "    }\n",
-      "  ],\n",
-      "  \"scheduled_actions\": [\n",
-      "    {\n",
-      "      \"hour\": 6,\n",
-      "      \"action_type\": \"post\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"tips for morning routines\",\n",
-      "      \"tags\": [\"morning\", \"routines\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    },\n",
-      "    {\n",
-      "      \"hour\": 8,\n",
-      "      \"action_type\": \"create_content\",\n",
-      "      \"content_type\": \"reel\",\n",
-      "      \"topic\": \"best workout routines\",\n",
-      "      \"tags\": [\"fitness\", \"exercises\"],\n",
-      "      \"intent\": \"watch_bait\"\n",
-      "    }\n",
-      "  ],\n",
-      "  \"notes\": \"Proposed collaboration opportunity to reach broader audience.\"\n",
-      "}\n",
-      "```\n",
-      "============================================================\n",
-      "    Day  7: r=0.2163 e=1.00 posts=1 tools=1\n",
-      "  => grader=0.3621 reward=1.507\n",
+      "  Training loss: 1.9805\n",
       "\n",
-      "============================================================\n",
-      "AFTER TRAINING:\n",
-      "  monthly_engage: grader=0.0162\n",
-      "  monthly_strategic: grader=0.1749\n",
-      "  monthly_competitive: grader=0.3621\n"
+      "Training complete in 21.5 min\n",
+      " round  avg_episode_reward  max_episode_reward  min_episode_reward  avg_grader  max_grader  n_training_samples  train_loss\n",
+      "     1               3.154               4.348               2.316      0.3017      0.6470                  38      2.6893\n",
+      "     2               2.855               3.710               2.249      0.2058      0.5125                  37      2.5540\n",
+      "     3               2.969               4.140               2.250      0.2041      0.4656                  45      2.0757\n",
+      "     4               3.294               4.527               2.320      0.2793      0.5243                  45      1.9805\n"
      ]
     }
    ],
    "source": [
-    "# Cell 12: Run trained model\n",
-    "print(\"Running TRAINED model on all tasks...\")\n",
-    "print(\"=\" * 60)\n",
+    "# Cell 11: Training loop\n",
+    "from trl import SFTTrainer, SFTConfig\n",
+    "from datasets import Dataset\n",
     "\n",
-    "peft_model.eval()\n",
-    "after_results = {}\n",
-    "for task in TASKS:\n",
-    "    print(f\"\\n  Task: {task}\")\n",
-    "    result = run_llm_episode(peft_model, tokenizer, task, seed=42, verbose=True)\n",
-    "    after_results[task] = result\n",
-    "    print(f\"  => grader={result['grader_score']:.4f} reward={result['total_reward']:.3f}\")\n",
+    "NUM_ROUNDS = 4\n",
+    "EPISODES_PER_ROUND = 6\n",
+    "TOP_K_FRACTION = 0.5\n",
+    "\n",
+    "training_log = {\n",
+    "    \"round\": [], \"avg_episode_reward\": [], \"max_episode_reward\": [],\n",
+    "    \"min_episode_reward\": [], \"avg_grader\": [], \"max_grader\": [],\n",
+    "    \"n_training_samples\": [], \"train_loss\": [],\n",
+    "}\n",
+    "\n",
+    "t_start = time.time()\n",
+    "\n",
+    "for round_idx in range(1, NUM_ROUNDS + 1):\n",
+    "    print(f\"\\n{'=' * 60}\")\n",
+    "    print(f\"TRAINING ROUND {round_idx}/{NUM_ROUNDS}\")\n",
+    "    print(f\"{'=' * 60}\")\n",
+    "\n",
+    "    peft_model.eval()\n",
+    "    tasks_seeds = [(TASKS[ep % len(TASKS)], 42 + (round_idx - 1) * 100 + ep) for ep in range(EPISODES_PER_ROUND)]\n",
+    "    t_roll = time.time()\n",
+    "    results = run_llm_episodes_batched(peft_model, tokenizer, tasks_seeds, verbose=False)\n",
+    "    print(f\"  Rollouts: {len(results)} eps × {TASK_HORIZON} days in {time.time()-t_roll:.1f}s\")\n",
+    "\n",
+    "    all_pairs, episode_rewards, episode_graders = [], [], []\n",
+    "    for ep, result in enumerate(results):\n",
+    "        ep_reward = result[\"total_reward\"] + 2.0 * result[\"grader_score\"]\n",
+    "        episode_rewards.append(ep_reward)\n",
+    "        episode_graders.append(result[\"grader_score\"])\n",
+    "        kept = 0\n",
+    "        for pr in result[\"pairs\"]:\n",
+    "            if not is_well_formed_response(pr[\"response\"]):\n",
+    "                continue\n",
+    "            text = (f\"<|im_start|>system\\n{SYSTEM_PROMPT}<|im_end|>\\n\"\n",
+    "                    f\"<|im_start|>user\\n{pr['prompt']}<|im_end|>\\n\"\n",
+    "                    f\"<|im_start|>assistant\\n{pr['response']}<|im_end|>\")\n",
+    "            all_pairs.append({\"text\": text, \"reward\": pr[\"return\"]})\n",
+    "            kept += 1\n",
+    "        print(f\"  ep {ep+1}/{EPISODES_PER_ROUND}: {result['task'].split('_')[-1]:>11s} \"\n",
+    "              f\"grader={result['grader_score']:.4f} reward={ep_reward:.3f} kept={kept}/{len(result['pairs'])}\")\n",
+    "\n",
+    "    avg_r = float(np.mean(episode_rewards))\n",
+    "    avg_g = float(np.mean(episode_graders))\n",
+    "    print(f\"  Avg reward={avg_r:.3f} Avg grader={avg_g:.4f} | total pairs={len(all_pairs)}\")\n",
+    "    if not all_pairs:\n",
+    "        print(\"  WARNING: 0 well-formed pairs collected; skipping SFT.\")\n",
+    "        continue\n",
+    "\n",
+    "    threshold = np.percentile([p[\"reward\"] for p in all_pairs], (1 - TOP_K_FRACTION) * 100)\n",
+    "    filtered = [p for p in all_pairs if p[\"reward\"] >= threshold] or all_pairs\n",
+    "    print(f\"  Filtered to {len(filtered)}/{len(all_pairs)} samples (return >= {threshold:.3f})\")\n",
+    "\n",
+    "    dataset = Dataset.from_list([{\"text\": p[\"text\"]} for p in filtered])\n",
+    "\n",
+    "    # SFT training (real gradient updates)\n",
+    "    sft_config = SFTConfig(\n",
+    "        output_dir=f\"./checkpoints/round_{round_idx}\",\n",
+    "        num_train_epochs=2,\n",
+    "        per_device_train_batch_size=4,\n",
+    "        gradient_accumulation_steps=2,\n",
+    "        learning_rate=2e-5,\n",
+    "        warmup_ratio=0.1,\n",
+    "        logging_steps=1,\n",
+    "        save_strategy=\"no\",\n",
+    "        max_length=4096,\n",
+    "        bf16=True,\n",
+    "        report_to=\"none\",\n",
+    "    )\n",
+    "\n",
+    "    peft_model.train()\n",
+    "    trainer = SFTTrainer(\n",
+    "        model=peft_model, processing_class=tokenizer,\n",
+    "        train_dataset=dataset, args=sft_config,\n",
+    "    )\n",
+    "    train_result = trainer.train()\n",
+    "    loss = train_result.training_loss\n",
+    "    print(f\"  Training loss: {loss:.4f}\")\n",
+    "\n",
+    "    training_log[\"round\"].append(round_idx)\n",
+    "    training_log[\"avg_episode_reward\"].append(round(float(avg_r), 3))\n",
+    "    training_log[\"max_episode_reward\"].append(round(float(max(episode_rewards)), 3))\n",
+    "    training_log[\"min_episode_reward\"].append(round(float(min(episode_rewards)), 3))\n",
+    "    training_log[\"avg_grader\"].append(round(float(avg_g), 4))\n",
+    "    training_log[\"max_grader\"].append(round(float(max(episode_graders)), 4))\n",
+    "    training_log[\"n_training_samples\"].append(len(filtered))\n",
+    "    training_log[\"train_loss\"].append(round(loss, 4))\n",
+    "\n",
+    "elapsed = time.time() - t_start\n",
+    "print(f\"\\nTraining complete in {elapsed/60:.1f} min\")\n",
+    "print(pd.DataFrame(training_log).to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa127c02",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006508,
+     "end_time": "2026-04-26T01:22:58.485976+00:00",
+     "exception": false,
+     "start_time": "2026-04-26T01:22:58.479468+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Part 5: Trained LLM Evaluation (“After”)\n",
+    "\n",
+    "Same model, same seeds, same environment — but now with updated LoRA weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1c89f1d2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-04-26T01:22:58.498923Z",
+     "iopub.status.busy": "2026-04-26T01:22:58.498706Z",
+     "iopub.status.idle": "2026-04-26T01:30:06.678934Z",
+     "shell.execute_reply": "2026-04-26T01:30:06.678154Z"
+    },
+    "papermill": {
+     "duration": 428.193492,
+     "end_time": "2026-04-26T01:30:06.685475+00:00",
+     "exception": false,
+     "start_time": "2026-04-26T01:22:58.491983+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running TRAINED model on all tasks (batched)...\n",
+      "============================================================\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D 1: batch=3 rest=0 prompt_tok=495\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D 2: batch=3 rest=0 prompt_tok=786\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D 3: batch=3 rest=0 prompt_tok=1075\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D 4: batch=3 rest=0 prompt_tok=1374\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D 5: batch=3 rest=0 prompt_tok=1672\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D 6: batch=3 rest=0 prompt_tok=2074\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D 7: batch=3 rest=0 prompt_tok=2543\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D 8: batch=3 rest=0 prompt_tok=3068\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D 9: batch=3 rest=0 prompt_tok=3383\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D10: batch=3 rest=0 prompt_tok=3695\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D11: batch=3 rest=0 prompt_tok=3998\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D12: batch=3 rest=0 prompt_tok=4302\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D13: batch=3 rest=0 prompt_tok=4503\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D14: batch=3 rest=0 prompt_tok=4635\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  D15: batch=3 rest=0 prompt_tok=4712\n",
+      "\n",
+      "============================================================\n",
+      "AFTER TRAINING (took 428.2s):\n",
+      "  monthly_engage: grader=0.1071\n",
+      "  monthly_strategic: grader=0.3174\n",
+      "  monthly_competitive: grader=0.5233\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cell 12: Run trained model (batched)\n",
+    "print(\"Running TRAINED model on all tasks (batched)...\")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "peft_model.eval()\n",
+    "t0 = time.time()\n",
+    "results = run_llm_episodes_batched(peft_model, tokenizer, [(t, 42) for t in TASKS], verbose=True)\n",
+    "after_results = {r[\"task\"]: r for r in results}\n",
     "\n",
     "print(\"\\n\" + \"=\" * 60)\n",
-    "print(\"AFTER TRAINING:\")\n",
+    "print(f\"AFTER TRAINING (took {time.time()-t0:.1f}s):\")\n",
     "for t in TASKS:\n",
     "    print(f\"  {t}: grader={after_results[t]['grader_score']:.4f}\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e3e9a4ab",
+   "id": "37679000",
    "metadata": {
     "papermill": {
-     "duration": 0.007082,
-     "end_time": "2026-04-25T23:01:26.553021+00:00",
+     "duration": 0.006378,
+     "end_time": "2026-04-26T01:30:06.698601+00:00",
      "exception": false,
-     "start_time": "2026-04-25T23:01:26.545939+00:00",
+     "start_time": "2026-04-26T01:30:06.692223+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4199,19 +2222,19 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "eceb46eb",
+   "id": "05832c56",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T23:01:26.568471Z",
-     "iopub.status.busy": "2026-04-25T23:01:26.568240Z",
-     "iopub.status.idle": "2026-04-25T23:01:26.905154Z",
-     "shell.execute_reply": "2026-04-25T23:01:26.904198Z"
+     "iopub.execute_input": "2026-04-26T01:30:06.713001Z",
+     "iopub.status.busy": "2026-04-26T01:30:06.712787Z",
+     "iopub.status.idle": "2026-04-26T01:30:07.080646Z",
+     "shell.execute_reply": "2026-04-26T01:30:07.079755Z"
     },
     "papermill": {
-     "duration": 0.345619,
-     "end_time": "2026-04-25T23:01:26.905758+00:00",
+     "duration": 0.375605,
+     "end_time": "2026-04-26T01:30:07.081254+00:00",
      "exception": false,
-     "start_time": "2026-04-25T23:01:26.560139+00:00",
+     "start_time": "2026-04-26T01:30:06.705649+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4219,7 +2242,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1400x500 with 2 Axes>"
       ]
@@ -4254,19 +2277,19 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "7920e23a",
+   "id": "ceb73f34",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T23:01:26.922627Z",
-     "iopub.status.busy": "2026-04-25T23:01:26.922445Z",
-     "iopub.status.idle": "2026-04-25T23:01:27.180249Z",
-     "shell.execute_reply": "2026-04-25T23:01:27.179391Z"
+     "iopub.execute_input": "2026-04-26T01:30:07.096654Z",
+     "iopub.status.busy": "2026-04-26T01:30:07.096498Z",
+     "iopub.status.idle": "2026-04-26T01:30:07.326696Z",
+     "shell.execute_reply": "2026-04-26T01:30:07.325963Z"
     },
     "papermill": {
-     "duration": 0.266997,
-     "end_time": "2026-04-25T23:01:27.180912+00:00",
+     "duration": 0.238562,
+     "end_time": "2026-04-26T01:30:07.327271+00:00",
      "exception": false,
-     "start_time": "2026-04-25T23:01:26.913915+00:00",
+     "start_time": "2026-04-26T01:30:07.088709+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4274,7 +2297,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -4317,19 +2340,19 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "c7d0adde",
+   "id": "7749a0f8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T23:01:27.198624Z",
-     "iopub.status.busy": "2026-04-25T23:01:27.198440Z",
-     "iopub.status.idle": "2026-04-25T23:01:28.118265Z",
-     "shell.execute_reply": "2026-04-25T23:01:28.117532Z"
+     "iopub.execute_input": "2026-04-26T01:30:07.343987Z",
+     "iopub.status.busy": "2026-04-26T01:30:07.343827Z",
+     "iopub.status.idle": "2026-04-26T01:30:08.287702Z",
+     "shell.execute_reply": "2026-04-26T01:30:08.286899Z"
     },
     "papermill": {
-     "duration": 0.929714,
-     "end_time": "2026-04-25T23:01:28.118907+00:00",
+     "duration": 0.952996,
+     "end_time": "2026-04-26T01:30:08.288446+00:00",
      "exception": false,
-     "start_time": "2026-04-25T23:01:27.189193+00:00",
+     "start_time": "2026-04-26T01:30:07.335450+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4337,7 +2360,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x800 with 6 Axes>"
       ]
@@ -4373,13 +2396,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2f368b2",
+   "id": "50284f4d",
    "metadata": {
     "papermill": {
-     "duration": 0.009334,
-     "end_time": "2026-04-25T23:01:28.138384+00:00",
+     "duration": 0.009081,
+     "end_time": "2026-04-26T01:30:08.307718+00:00",
      "exception": false,
-     "start_time": "2026-04-25T23:01:28.129050+00:00",
+     "start_time": "2026-04-26T01:30:08.298637+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4391,19 +2414,19 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "85bff6c3",
+   "id": "7e19386e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T23:01:28.157495Z",
-     "iopub.status.busy": "2026-04-25T23:01:28.157286Z",
-     "iopub.status.idle": "2026-04-25T23:01:28.166686Z",
-     "shell.execute_reply": "2026-04-25T23:01:28.165716Z"
+     "iopub.execute_input": "2026-04-26T01:30:08.343124Z",
+     "iopub.status.busy": "2026-04-26T01:30:08.342917Z",
+     "iopub.status.idle": "2026-04-26T01:30:08.352073Z",
+     "shell.execute_reply": "2026-04-26T01:30:08.351228Z"
     },
     "papermill": {
-     "duration": 0.020136,
-     "end_time": "2026-04-25T23:01:28.167527+00:00",
+     "duration": 0.019887,
+     "end_time": "2026-04-26T01:30:08.352892+00:00",
      "exception": false,
-     "start_time": "2026-04-25T23:01:28.147391+00:00",
+     "start_time": "2026-04-26T01:30:08.333005+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4419,11 +2442,11 @@
       "\n",
       "Task                          Before      After      Delta      Smart\n",
       "-------------------------------------------------------------------\n",
-      "monthly_engage                0.3048     0.0162    -0.2886     0.6342\n",
-      "monthly_strategic             0.3456     0.1749    -0.1707     0.7218\n",
-      "monthly_competitive           0.4808     0.3621    -0.1187     0.8315\n",
+      "monthly_engage                0.5642     0.1071    -0.4571     0.7352\n",
+      "monthly_strategic             0.5903     0.3174    -0.2729     0.9043\n",
+      "monthly_competitive           0.8313     0.5233    -0.3080     0.9066\n",
       "-------------------------------------------------------------------\n",
-      "AVERAGE                       0.3771     0.1844    -0.1927     0.7292\n",
+      "AVERAGE                       0.6619     0.3159    -0.3460     0.8487\n",
       "\n",
       "Saved to /work/plots/\n",
       "All results are from real LoRA weight updates on real environment runs.\n"
@@ -4471,19 +2494,19 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "7f92e1fa",
+   "id": "61013d6d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-25T23:01:28.187790Z",
-     "iopub.status.busy": "2026-04-25T23:01:28.187587Z",
-     "iopub.status.idle": "2026-04-25T23:01:28.400854Z",
-     "shell.execute_reply": "2026-04-25T23:01:28.399866Z"
+     "iopub.execute_input": "2026-04-26T01:30:08.371696Z",
+     "iopub.status.busy": "2026-04-26T01:30:08.371518Z",
+     "iopub.status.idle": "2026-04-26T01:30:08.703883Z",
+     "shell.execute_reply": "2026-04-26T01:30:08.702953Z"
     },
     "papermill": {
-     "duration": 0.224623,
-     "end_time": "2026-04-25T23:01:28.401486+00:00",
+     "duration": 0.342614,
+     "end_time": "2026-04-26T01:30:08.704499+00:00",
      "exception": false,
-     "start_time": "2026-04-25T23:01:28.176863+00:00",
+     "start_time": "2026-04-26T01:30:08.361885+00:00",
      "status": "completed"
     },
     "tags": []
@@ -4530,20 +2553,2684 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 852.127164,
-   "end_time": "2026-04-25T23:01:31.530152+00:00",
+   "duration": 1859.700012,
+   "end_time": "2026-04-26T01:30:11.309089+00:00",
    "environment_variables": {},
    "exception": null,
    "input_path": "training/train_grpo.ipynb",
    "output_path": "training/train_grpo.executed.ipynb",
    "parameters": {},
-   "start_time": "2026-04-25T22:47:19.402988+00:00",
+   "start_time": "2026-04-26T00:59:11.609077+00:00",
    "version": "2.7.0"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "06b28508d49e45d29e2892bbbad9415c": {
+     "00bc8d66e36a4050ae315c58b2e230aa": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "013c2f294e10458f9c812fe267dac401": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "04d5dc716f404fa3a497b8aefacb082f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "081ad6289f7a43c989f755fbf33f24ed": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "0a9867373b3a40179290534593d1867e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_85262158ac15470fa2a986b3625ea8d5",
+       "placeholder": "​",
+       "style": "IPY_MODEL_7a24ffffcdcd4ea5ae379b2e7daf368f",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 38/38 [00:00&lt;00:00, 725.49 examples/s]"
+      }
+     },
+     "0b3cb525050f456faebabe631ed96a4f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "0c935347f20a4d21af6e8187d57c7ffc": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_fca11b43c8fd492689df1a22840f6b22",
+       "max": 434.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_45b9e231b9154cb9a0f1d3181265bec3",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 434.0
+      }
+     },
+     "0d035fc2567b4e0885647ad64006a05b": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "10efe31aacd74e708b8921493b1a8fe0": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "116355bcbe814c55a0d1bfb0dbac98c2": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_5de4f64c149f4800b55e92ef41e9aaae",
+       "placeholder": "​",
+       "style": "IPY_MODEL_5e959358110b4047bb6a7044526e7f64",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "Tokenizing train dataset: 100%"
+      }
+     },
+     "129cdd1b47a04c30a61a002f8248a0e0": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "15712b7dc4e744cc926f528aada5a770": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_6f3905e603a04c97a596dfbaca8475c3",
+        "IPY_MODEL_7198f511f4ce457da5510a079e0d3f79",
+        "IPY_MODEL_4ee6808bf1b2455c95ed59941a16402e"
+       ],
+       "layout": "IPY_MODEL_911ed45c786948c5901f4ecc82f79bc7",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "169a1d30c534414599ef60ea5930b306": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "1bc6a332dbca4bef8696f1da49cb1ebf": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_116355bcbe814c55a0d1bfb0dbac98c2",
+        "IPY_MODEL_5e96b22266284a308936c4c1aa4beb46",
+        "IPY_MODEL_c9d1121116704df282befbbd64a0399c"
+       ],
+       "layout": "IPY_MODEL_4d6a7f8be86940a1851bfc9e9e143b1c",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "1ccd04af6d4f4a548bdb9f97e85ad6d4": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_49f39beff7374b1e89390cc963534aab",
+        "IPY_MODEL_cc826df8445d4a55b2b15ec2298a3841",
+        "IPY_MODEL_5676647630754ddfb3a4f14c314a2597"
+       ],
+       "layout": "IPY_MODEL_5a307c4ddd714e3b955c41dbaf832199",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "1d08b23ba7c744c7a49acfff2c7413f1": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "1e103da95fb7451bb45e891eb3b77cd9": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "1ea298381876407793a39f6acc97c721": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "1f3882c60eaa4afb863623220511cd85": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "1fd815879a3b492cae2307142253b678": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "209b910e79894ecd8b415071b8e5643a": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_6abcf3c0891c43049a0160e6d0b19147",
+        "IPY_MODEL_9ed0996f20924056a20fb22ce91540ce",
+        "IPY_MODEL_b136c377d78946f599e1eb81b95942bc"
+       ],
+       "layout": "IPY_MODEL_b92002ece3f442e4bc1098a2f94aa8a0",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "21ac1bd8b4674f3d90b234950a0b4bb2": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "22fb3099714f4fc0a7263469a5d37779": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_d4be0d5754174b68b1846237af1ffad4",
+        "IPY_MODEL_c6375ffd3bf146d4b49e2b5f76d63df7",
+        "IPY_MODEL_624b0b164c194b8da79bc85f88160814"
+       ],
+       "layout": "IPY_MODEL_695a46f7397c40199194f9d910c23fed",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "24f01fe67323449f862f99814aad6d77": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "26ec3845f8cc46e3979738676ad6a5f7": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "28120ddaec6a4dc98af1d11cc2c1363d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_54868b26295c4e91bdc90a2569a18249",
+        "IPY_MODEL_de9bb66f5c234904b57c3d68290fb67f",
+        "IPY_MODEL_c989786e5799465d9d28c1f137b9e972"
+       ],
+       "layout": "IPY_MODEL_c45283202f3a4261891488f4e281ac22",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "291f21ecf9cd4e8187c8f9ba11f14742": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "2ab08093b53a4cf184633770536b5e14": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "2bf895071502432d8e9fa99c4c16723e": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "2cd08a2671ec4fe7b29cc36c0c081260": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_2e544226f0f44e65b03d40ff13c47540",
+       "placeholder": "​",
+       "style": "IPY_MODEL_b9bd8a943e684e91971bbb9eeb365f93",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "Download complete: 100%"
+      }
+     },
+     "2e022e459d974bb3a16191809edc7678": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_572163fcd00c4508be7dd44bec01186e",
+       "placeholder": "​",
+       "style": "IPY_MODEL_6a291105c21b44f2bed3a79b1b4c0f4c",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "Fetching 2 files: 100%"
+      }
+     },
+     "2e544226f0f44e65b03d40ff13c47540": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "2fe28de0184d4a519b30b401fb37a86f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_8fdf8a139306491db504aacec3b168cd",
+       "placeholder": "​",
+       "style": "IPY_MODEL_a802f00411aa47f88ebd9645cad9e595",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 7.03M/7.03M [00:00&lt;00:00, 26.0MB/s]"
+      }
+     },
+     "3041ace8076e4ce2bd2f15a5c9a79240": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "3208b1140d444cd49050a8aed9295f94": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "3293ffd6eee54f6284f2d22e4730c457": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "32a7b96d0355416a82514b66ef56e96f": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "33f2af0d5ef54325b26c36a5faa39958": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "3623df644d3b40bea2f053e0d6c6f982": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "3a3e24a1bde7455eaf9d3c6b63cb0ac6": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "3ac78502b3d64574846c856320361fb5": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_32a7b96d0355416a82514b66ef56e96f",
+       "placeholder": "​",
+       "style": "IPY_MODEL_d6cb204c4cbe408890c4faf682a80558",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 7.30k/7.30k [00:00&lt;00:00, 1.43MB/s]"
+      }
+     },
+     "3bcb98cc398145bcaf9b73d09e028d1c": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "4029d6b81ac34c178bfd4238a297046c": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_2cd08a2671ec4fe7b29cc36c0c081260",
+        "IPY_MODEL_c52288ca86e94235b7d0038f8199891a",
+        "IPY_MODEL_e1f35d1cd78449aea09331ab27fef1f0"
+       ],
+       "layout": "IPY_MODEL_3623df644d3b40bea2f053e0d6c6f982",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "4063dc04bb5747f88ce9582649466092": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_a426072d054f475ea15d4260c6fae606",
+       "placeholder": "​",
+       "style": "IPY_MODEL_a90ccde3981f4726b48dbdd8d171fd57",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 45/45 [00:00&lt;00:00, 5742.13 examples/s]"
+      }
+     },
+     "40af92436a5b4fb1b2be07178c408c26": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "45b9e231b9154cb9a0f1d3181265bec3": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "46037f140e0044449ab1e4dafa27de2c": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_c7a7063d0ba04c05b8e8b807c33e2a0c",
+       "max": 35581.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_fa037963a762456f9059c4924d070a4d",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 35581.0
+      }
+     },
+     "462b2d545c07419a89f23aa4fdec943d": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "49f39beff7374b1e89390cc963534aab": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_1f3882c60eaa4afb863623220511cd85",
+       "placeholder": "​",
+       "style": "IPY_MODEL_0b3cb525050f456faebabe631ed96a4f",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "Adding EOS to train dataset: 100%"
+      }
+     },
+     "4a541616398c4fbb8ed7a06864392dcb": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_507e7c5c993049698c953b80d21b4201",
+       "placeholder": "​",
+       "style": "IPY_MODEL_780a022146b345e2b13a201e49094090",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "tokenizer.json: 100%"
+      }
+     },
+     "4b5fc2522a094c9d9d0d3e239f92b557": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_7df5cb17c98e472eb7a79f9c84bb2bb6",
+       "placeholder": "​",
+       "style": "IPY_MODEL_24f01fe67323449f862f99814aad6d77",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 242/242 [00:00&lt;00:00, 57.5kB/s]"
+      }
+     },
+     "4c08c2d5f4ee4f178435afd243c30878": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "4d6a7f8be86940a1851bfc9e9e143b1c": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "4ee6808bf1b2455c95ed59941a16402e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_f149f0e7f0ed4ef6804e532c36b99eb9",
+       "placeholder": "​",
+       "style": "IPY_MODEL_04d5dc716f404fa3a497b8aefacb082f",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 45/45 [00:00&lt;00:00, 4830.66 examples/s]"
+      }
+     },
+     "504b26a967ef4b3c9f80251edb33e15e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "507e7c5c993049698c953b80d21b4201": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "50c654243e5240899af5b948b25722f7": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "50ea0b2ecdaa4ed5baaa21be180f4cf3": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_743085f389504fa98f479ea872b08a6d",
+       "max": 7031645.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_6402c823ad19440a87a957024a415c28",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 7031645.0
+      }
+     },
+     "54868b26295c4e91bdc90a2569a18249": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_aca9b92fe28544429e3aeb37f953476a",
+       "placeholder": "​",
+       "style": "IPY_MODEL_504b26a967ef4b3c9f80251edb33e15e",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "config.json: 100%"
+      }
+     },
+     "55862e5d38bb45aabdbb5cdb2aa78dcd": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_ddbf9647c7a648bc9085b7bbd2b0c8c6",
+        "IPY_MODEL_d7a5dfeae2c740d88a90b484109d8e2c",
+        "IPY_MODEL_4063dc04bb5747f88ce9582649466092"
+       ],
+       "layout": "IPY_MODEL_129cdd1b47a04c30a61a002f8248a0e0",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "5676647630754ddfb3a4f14c314a2597": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_aafbac41be004d218cfd0d32b779cc73",
+       "placeholder": "​",
+       "style": "IPY_MODEL_59925f4280d44cf4b1f190780a6638b2",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 38/38 [00:00&lt;00:00, 4731.73 examples/s]"
+      }
+     },
+     "572163fcd00c4508be7dd44bec01186e": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "57e16ebbf2f74bde973dc7344c4b6628": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_c5f692d0f4b8496c84da7bed309db3f7",
+       "placeholder": "​",
+       "style": "IPY_MODEL_ba9d2e4c10794a85a17a3b89c9f7ef27",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 2.78M/2.78M [00:00&lt;00:00, 132MB/s]"
+      }
+     },
+     "59925f4280d44cf4b1f190780a6638b2": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "59da4931c81446a495d8b4eceab2998b": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "5a307c4ddd714e3b955c41dbaf832199": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "5da72d1a02364074ab84e97896892343": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_a21813e4175e4648b3a1103e1abd6dcf",
+       "placeholder": "​",
+       "style": "IPY_MODEL_9b7db0f51e954fb9b861fcf7f313903c",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "vocab.json: 100%"
+      }
+     },
+     "5de4f64c149f4800b55e92ef41e9aaae": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "5df5558dde33430687056f9455ac6ede": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "5e292052f0e44ddfb7d8381a3851e369": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "5e959358110b4047bb6a7044526e7f64": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "5e96b22266284a308936c4c1aa4beb46": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_94d6f60b7a23416f81648d340b18bdd0",
+       "max": 45.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_9a2ab03bdf35411ea6dea73a0c4b9273",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 45.0
+      }
+     },
+     "601fcf7b6b5244258dbf9153c387b9f8": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "609d81288c1f40d994bb5360ba3c6f9e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_65dcdfafb56e4219a32b2ca067c4d246",
+       "placeholder": "​",
+       "style": "IPY_MODEL_907e0be088a94b709fc69ae46fa3cc38",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 2/2 [00:07&lt;00:00,  7.85s/it]"
+      }
+     },
+     "624b0b164c194b8da79bc85f88160814": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_dd5d8309949b46209ca763fab0f345f0",
+       "placeholder": "​",
+       "style": "IPY_MODEL_1e103da95fb7451bb45e891eb3b77cd9",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 1.67M/1.67M [00:00&lt;00:00, 22.6MB/s]"
+      }
+     },
+     "6265673735ab47bca14d5cec6f258f5f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_8ba63b3bb8994fc49d70ae0a84bed4ef",
+       "placeholder": "​",
+       "style": "IPY_MODEL_8cd249ccd8b64f02a1499e06b2dad958",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "tokenizer_config.json: 100%"
+      }
+     },
+     "63df809a5046457988c3c10df79880db": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "6402c823ad19440a87a957024a415c28": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "65dcdfafb56e4219a32b2ca067c4d246": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "695a46f7397c40199194f9d910c23fed": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     },
+     "6a291105c21b44f2bed3a79b1b4c0f4c": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "6abcf3c0891c43049a0160e6d0b19147": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -4558,51 +5245,91 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_96c5aa840bc24103a3cde64f31f17190",
+       "layout": "IPY_MODEL_81b7d0b2058e405ea803d606fb39528e",
        "placeholder": "​",
-       "style": "IPY_MODEL_f5cb210961614af3a2c0372c8dd5427f",
+       "style": "IPY_MODEL_10efe31aacd74e708b8921493b1a8fe0",
        "tabbable": null,
        "tooltip": null,
-       "value": " 660/660 [00:00&lt;00:00, 144kB/s]"
+       "value": "Tokenizing train dataset: 100%"
       }
      },
-     "07b571e339044dd99c9a7bcc53c29b70": {
-      "model_module": "@jupyter-widgets/controls",
+     "6b4fde19aab54b24a186e4a69bb5fa1a": {
+      "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
+      "model_name": "LayoutModel",
       "state": {
-       "_model_module": "@jupyter-widgets/controls",
+       "_model_module": "@jupyter-widgets/base",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
+       "_model_name": "LayoutModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "0b057843dd8145e197affdb1b54d5c11": {
+     "6f3905e603a04c97a596dfbaca8475c3": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
+      "model_name": "HTMLModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
+       "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_b00addf3a00c49fe856c02912bb81820",
+       "placeholder": "​",
+       "style": "IPY_MODEL_081ad6289f7a43c989f755fbf33f24ed",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "Adding EOS to train dataset: 100%"
       }
      },
-     "116318f0fd074405bce4f0a9f70606e3": {
+     "7198f511f4ce457da5510a079e0d3f79": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "FloatProgressModel",
@@ -4618,33 +5345,41 @@
        "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_92572de3c8bd4340920bd41041fa3677",
-       "max": 4,
-       "min": 0,
+       "layout": "IPY_MODEL_33f2af0d5ef54325b26c36a5faa39958",
+       "max": 45.0,
+       "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_461954ccde12450b884a5d88d99f78cc",
+       "style": "IPY_MODEL_169a1d30c534414599ef60ea5930b306",
        "tabbable": null,
        "tooltip": null,
-       "value": 4
+       "value": 45.0
       }
      },
-     "12cd3cee31124f45a49c0b1f6b6dabc2": {
+     "71fbda7384cd4d59abb1205d66c5a6dd": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "ProgressStyleModel",
+      "model_name": "HBoxModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "ProgressStyleModel",
+       "_model_name": "HBoxModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
+       "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_b24eb59d45114caba4ccf8daf786fbbd",
+        "IPY_MODEL_0c935347f20a4d21af6e8187d57c7ffc",
+        "IPY_MODEL_f2101446750d49aa8111e6dc1270ba48"
+       ],
+       "layout": "IPY_MODEL_c9e6ad722fbe46c7a25d0eccd6d29cfe",
+       "tabbable": null,
+       "tooltip": null
       }
      },
-     "1444f5ba0f92425d81dc3aea332fdfc8": {
+     "743085f389504fa98f479ea872b08a6d": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -4697,7 +5432,7 @@
        "width": null
       }
      },
-     "15764aef8e034dcc89c5b3b43a07366c": {
+     "763ddff7ad0f487e93ff6a28dc0147b8": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -4750,7 +5485,7 @@
        "width": null
       }
      },
-     "1a5abea34e494dc683603229b7a65d73": {
+     "77aa49df8e5e43dcbe3140c8bb2a01b1": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -4803,7 +5538,67 @@
        "width": null
       }
      },
-     "1aab4e7be84b41e0bb4c3c5d1078bd88": {
+     "780a022146b345e2b13a201e49094090": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "7a24ffffcdcd4ea5ae379b2e7daf368f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "7dedf106768641e284dfe3a69d3681b6": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_4a541616398c4fbb8ed7a06864392dcb",
+        "IPY_MODEL_50ea0b2ecdaa4ed5baaa21be180f4cf3",
+        "IPY_MODEL_2fe28de0184d4a519b30b401fb37a86f"
+       ],
+       "layout": "IPY_MODEL_912433733d2140f78ff034d5713e1365",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "7df5cb17c98e472eb7a79f9c84bb2bb6": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -4856,94 +5651,86 @@
        "width": null
       }
      },
-     "1c3cba90df624bfaa6b8fd950e834ed1": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_f9aa2255c1094523a28e4e5850013ff3",
-        "IPY_MODEL_793c3fefdcf44710b5b32d396931b1a0",
-        "IPY_MODEL_4fcb0ece4db24d1ab44367762587864b"
-       ],
-       "layout": "IPY_MODEL_cbe219db8818475cb825daf8a3d3e838",
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "1dd3d7bbb92a4f2b84f86cf873e42fb7": {
+     "7f1f7f62515f4d219a552c55179c7c39": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
+      "model_name": "FloatProgressModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "FloatProgressModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_e338db5125d24f57a0e58d905b1aacb0",
-       "placeholder": "​",
-       "style": "IPY_MODEL_9242d318a92f41adae61428fd06845bd",
+       "layout": "IPY_MODEL_63df809a5046457988c3c10df79880db",
+       "max": 7305.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_9b782be2683b4c95a5ce764c3a6270da",
        "tabbable": null,
        "tooltip": null,
-       "value": "tokenizer_config.json: 100%"
-      }
-     },
-     "2161c6ddcfb6452a9ec8dab562ef47cb": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_63703c8b138f4a11a753446120a0e908",
-        "IPY_MODEL_ebb37c4d91bf4a42a7aef1e8186062e9",
-        "IPY_MODEL_9fe4c015f8e64fa7bd1e12664f74dbcd"
-       ],
-       "layout": "IPY_MODEL_ce107e335c1e41a9b9d0aeafb578c4b8",
-       "tabbable": null,
-       "tooltip": null
+       "value": 7305.0
       }
      },
-     "24e1b952b30a4266bae74d029c52d7f6": {
-      "model_module": "@jupyter-widgets/controls",
+     "81b7d0b2058e405ea803d606fb39528e": {
+      "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
-      "model_name": "ProgressStyleModel",
+      "model_name": "LayoutModel",
       "state": {
-       "_model_module": "@jupyter-widgets/controls",
+       "_model_module": "@jupyter-widgets/base",
        "_model_module_version": "2.0.0",
-       "_model_name": "ProgressStyleModel",
+       "_model_name": "LayoutModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "27d193479919478abccf42607a6b3032": {
+     "85262158ac15470fa2a986b3625ea8d5": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -4996,7 +5783,7 @@
        "width": null
       }
      },
-     "27f9c44f0b7d4bd89404461be6dbff21": {
+     "86e221d182824762992d512c1c2f8b18": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -5049,49 +5836,66 @@
        "width": null
       }
      },
-     "28fbc46f8dc44a6c9c2e71ab97d58c02": {
+     "88d8921816b44b6d8b401d4897f64613": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "ProgressStyleModel",
+      "model_name": "HTMLStyleModel",
       "state": {
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "ProgressStyleModel",
+       "_model_name": "HTMLStyleModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
        "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "2c2c1141de114958983f8669528f55ca": {
+     "89d9da8bd78e4b599ad360d5c589c70f": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "8b0c58c46ad04ee7bf421517b0a9773b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "FloatProgressModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
+       "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_da32983d4d164c4a942e1fff2b234113",
-       "max": 4,
-       "min": 0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_24e1b952b30a4266bae74d029c52d7f6",
+       "layout": "IPY_MODEL_bd453c2475754202b484bac5ed9f2b69",
+       "placeholder": "​",
+       "style": "IPY_MODEL_88d8921816b44b6d8b401d4897f64613",
        "tabbable": null,
        "tooltip": null,
-       "value": 4
+       "value": "Adding EOS to train dataset: 100%"
       }
      },
-     "330301478152410481945768d907f057": {
+     "8ba63b3bb8994fc49d70ae0a84bed4ef": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -5144,7 +5948,25 @@
        "width": null
       }
      },
-     "357f6d6b21f4431abf6cfdc934d33ee1": {
+     "8cd249ccd8b64f02a1499e06b2dad958": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "8fdf8a139306491db504aacec3b168cd": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -5197,7 +6019,7 @@
        "width": null
       }
      },
-     "362856d11a494978ae3f667d68f09d3c": {
+     "907e0be088a94b709fc69ae46fa3cc38": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLStyleModel",
@@ -5215,33 +6037,7 @@
        "text_color": null
       }
      },
-     "376884b55ebc4c15aaf378561c1839a3": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "FloatProgressModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_4f783474f1204d358c36ef52457d3d64",
-       "max": 7305,
-       "min": 0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_8999ebf7ab524d72aa6508e2d027ecc1",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 7305
-      }
-     },
-     "37f812ce5e2347479778ffccab06cb54": {
+     "911ed45c786948c5901f4ecc82f79bc7": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -5294,51 +6090,7 @@
        "width": null
       }
      },
-     "387d6d2981a74ab3abbea7b352fbfb39": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "3a17efff967144278453b7b92a146189": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "FloatProgressModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_e6bf97b928c240afa63326920624d9f8",
-       "max": 1671839,
-       "min": 0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_4a50b585075b49399513484981060fef",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 1671839
-      }
-     },
-     "3adecb27e83942588088da579cb170b2": {
+     "912433733d2140f78ff034d5713e1365": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -5391,103 +6143,7 @@
        "width": null
       }
      },
-     "3bdc93fafa724bdcb00b515e1b33332b": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_73b47cd92e754d7e8ace7cbef0a5cb1c",
-       "placeholder": "​",
-       "style": "IPY_MODEL_ef27ba1ee51a46d6a5790f9623d59ac6",
-       "tabbable": null,
-       "tooltip": null,
-       "value": " 2.78M/2.78M [00:00&lt;00:00, 23.6MB/s]"
-      }
-     },
-     "42e7c29f98674f64a64c3abdb9b40679": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_15764aef8e034dcc89c5b3b43a07366c",
-       "placeholder": "​",
-       "style": "IPY_MODEL_ea9c9055255943aea8dd542e48b91266",
-       "tabbable": null,
-       "tooltip": null,
-       "value": "model.safetensors: 100%"
-      }
-     },
-     "443cc98027db4b7d88e561a442945c2d": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
-      }
-     },
-     "460b0c74040b4031a721d1768173940a": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "461954ccde12450b884a5d88d99f78cc": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
-      }
-     },
-     "4656772fbd9b4cada38159808c209f2d": {
+     "928d7051207b480b97cb3b0d283bde38": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLStyleModel",
@@ -5496,62 +6152,16 @@
        "_model_module_version": "2.0.0",
        "_model_name": "HTMLStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "46671a6dad1f44fd92c451cdbffa3214": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_a4e984e6b37c48b28d84acbb7f5fa9d7",
-       "placeholder": "​",
-       "style": "IPY_MODEL_c5338b61e2cc4f3d868ba2370a96b50c",
-       "tabbable": null,
-       "tooltip": null,
-       "value": " 3.09G/3.09G [00:06&lt;00:00, 657MB/s]"
-      }
-     },
-     "47358f9f932a40a8ba48961ef65d630b": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_7bd060f8373645e5aa91681002f1ed23",
-       "placeholder": "​",
-       "style": "IPY_MODEL_cbb206a606ed44229ac17289f17ed9c3",
-       "tabbable": null,
-       "tooltip": null,
-       "value": " 7.03M/7.03M [00:00&lt;00:00, 52.6MB/s]"
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "48800cfb03424a28a0b37c1c9fd075b6": {
+     "94d6f60b7a23416f81648d340b18bdd0": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -5604,7 +6214,48 @@
        "width": null
       }
      },
-     "4965af7bb8c74cb186d30e37d36ad8bd": {
+     "963fcac71d454a1c9929499535cbeeea": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_26ec3845f8cc46e3979738676ad6a5f7",
+       "placeholder": "​",
+       "style": "IPY_MODEL_291f21ecf9cd4e8187c8f9ba11f14742",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "generation_config.json: 100%"
+      }
+     },
+     "98551ddb2ded458b87fb0e346b77a2b3": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "9a2ab03bdf35411ea6dea73a0c4b9273": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "ProgressStyleModel",
@@ -5620,7 +6271,7 @@
        "description_width": ""
       }
      },
-     "4a50b585075b49399513484981060fef": {
+     "9b782be2683b4c95a5ce764c3a6270da": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "ProgressStyleModel",
@@ -5636,7 +6287,7 @@
        "description_width": ""
       }
      },
-     "4ed3d5a4a24e404996782992ce69cfd2": {
+     "9b7db0f51e954fb9b861fcf7f313903c": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLStyleModel",
@@ -5654,7 +6305,7 @@
        "text_color": null
       }
      },
-     "4f783474f1204d358c36ef52457d3d64": {
+     "9e108ca645ea44bfa3fd92bae857bd14": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -5707,76 +6358,59 @@
        "width": null
       }
      },
-     "4fcb0ece4db24d1ab44367762587864b": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_27f9c44f0b7d4bd89404461be6dbff21",
-       "placeholder": "​",
-       "style": "IPY_MODEL_362856d11a494978ae3f667d68f09d3c",
-       "tabbable": null,
-       "tooltip": null,
-       "value": " 338/338 [00:00&lt;00:00, 765.70it/s]"
-      }
-     },
-     "52508674e4c048d9a7e7e8f54af2d737": {
+     "9ed0996f20924056a20fb22ce91540ce": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
+      "model_name": "FloatProgressModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "FloatProgressModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_fb8ad84b44544cbea03e46d27758b6f6",
-       "placeholder": "​",
-       "style": "IPY_MODEL_d6dad142bf7a4c769012c057db8e12db",
+       "layout": "IPY_MODEL_3041ace8076e4ce2bd2f15a5c9a79240",
+       "max": 37.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_21ac1bd8b4674f3d90b234950a0b4bb2",
        "tabbable": null,
        "tooltip": null,
-       "value": "tokenizer.json: 100%"
+       "value": 37.0
       }
      },
-     "5371ede9564748a787ac66b9f2c8f534": {
+     "a020504b1e7a415fa1bbdac38aba88a8": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
+      "model_name": "FloatProgressModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "FloatProgressModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_a7bc4fd218054751ba10ed47a5128b4a",
-       "placeholder": "​",
-       "style": "IPY_MODEL_0b057843dd8145e197affdb1b54d5c11",
+       "layout": "IPY_MODEL_1ea298381876407793a39f6acc97c721",
+       "max": 45.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_e35e0481b51d44c68d7c2b0dac91899d",
        "tabbable": null,
        "tooltip": null,
-       "value": "Adding EOS to train dataset: 100%"
+       "value": 45.0
       }
      },
-     "5489ccd564ea46199e34eb18a73b1aee": {
+     "a21813e4175e4648b3a1103e1abd6dcf": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -5829,100 +6463,33 @@
        "width": null
       }
      },
-     "58711a6139e14b5e824ce7717eb87bb5": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_c70a5078ca8d4c32a4100fb3fc60853a",
-        "IPY_MODEL_3a17efff967144278453b7b92a146189",
-        "IPY_MODEL_f007d918dc6147869c69cd3a5bd3d2e5"
-       ],
-       "layout": "IPY_MODEL_ae7ba26d724b433c96831c533ee5583e",
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "603f6b5d27264d49a3070989a413ee83": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_bf872f9dac41450bb3758bc6720d6ce9",
-       "placeholder": "​",
-       "style": "IPY_MODEL_4656772fbd9b4cada38159808c209f2d",
-       "tabbable": null,
-       "tooltip": null,
-       "value": "vocab.json: 100%"
-      }
-     },
-     "63703c8b138f4a11a753446120a0e908": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_f4662c56e2a8457ca7398fee9bc60295",
-       "placeholder": "​",
-       "style": "IPY_MODEL_f3eb296230564402b0171e5d638d2037",
-       "tabbable": null,
-       "tooltip": null,
-       "value": "generation_config.json: 100%"
-      }
-     },
-     "641cca32d5f04d0fa1be55d6fe2f7b72": {
+     "a233ffa1cba6416a95e82eefac5e132c": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
+      "model_name": "FloatProgressModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "FloatProgressModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_5489ccd564ea46199e34eb18a73b1aee",
-       "placeholder": "​",
-       "style": "IPY_MODEL_07b571e339044dd99c9a7bcc53c29b70",
+       "layout": "IPY_MODEL_0d035fc2567b4e0885647ad64006a05b",
+       "max": 38.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_3293ffd6eee54f6284f2d22e4730c457",
        "tabbable": null,
        "tooltip": null,
-       "value": "config.json: 100%"
+       "value": 38.0
       }
      },
-     "6b265e7a4ffd45f2a28666521116af77": {
+     "a426072d054f475ea15d4260c6fae606": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -5975,133 +6542,77 @@
        "width": null
       }
      },
-     "6c33b2647c98485a8b87aac185e5a8af": {
+     "a6579dc1c201438eba9931aec8fce315": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
+      "model_name": "ProgressStyleModel",
       "state": {
-       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "ProgressStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
+       "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_96e0801ba0654a9cab5448742b946d54",
-       "placeholder": "​",
-       "style": "IPY_MODEL_c89c6918ae5f463eae3cf48b41f913b5",
-       "tabbable": null,
-       "tooltip": null,
-       "value": " 4/4 [00:00&lt;00:00, 573.07 examples/s]"
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
       }
      },
-     "6e2e90c8df324fcb8ceb6b633d1ceb65": {
+     "a802f00411aa47f88ebd9645cad9e595": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HBoxModel",
+      "model_name": "HTMLStyleModel",
       "state": {
-       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
+       "_model_name": "HTMLStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
+       "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_5371ede9564748a787ac66b9f2c8f534",
-        "IPY_MODEL_116318f0fd074405bce4f0a9f70606e3",
-        "IPY_MODEL_6c33b2647c98485a8b87aac185e5a8af"
-       ],
-       "layout": "IPY_MODEL_a4cc2830308b440bac5f69e757e5d511",
-       "tabbable": null,
-       "tooltip": null
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "6e874941b66a44919079f959eae61fc2": {
+     "a90ccde3981f4726b48dbdd8d171fd57": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
+      "model_name": "HTMLStyleModel",
       "state": {
-       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "FloatProgressModel",
+       "_model_name": "HTMLStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
+       "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_48800cfb03424a28a0b37c1c9fd075b6",
-       "max": 3087467144,
-       "min": 0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_12cd3cee31124f45a49c0b1f6b6dabc2",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 3087467144
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "73b47cd92e754d7e8ace7cbef0a5cb1c": {
-      "model_module": "@jupyter-widgets/base",
+     "a96bc65796884e14a457acabc61ac455": {
+      "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
+      "model_name": "HTMLStyleModel",
       "state": {
-       "_model_module": "@jupyter-widgets/base",
+       "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "LayoutModel",
+       "_model_name": "HTMLStyleModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "LayoutView",
-       "align_content": null,
-       "align_items": null,
-       "align_self": null,
-       "border_bottom": null,
-       "border_left": null,
-       "border_right": null,
-       "border_top": null,
-       "bottom": null,
-       "display": null,
-       "flex": null,
-       "flex_flow": null,
-       "grid_area": null,
-       "grid_auto_columns": null,
-       "grid_auto_flow": null,
-       "grid_auto_rows": null,
-       "grid_column": null,
-       "grid_gap": null,
-       "grid_row": null,
-       "grid_template_areas": null,
-       "grid_template_columns": null,
-       "grid_template_rows": null,
-       "height": null,
-       "justify_content": null,
-       "justify_items": null,
-       "left": null,
-       "margin": null,
-       "max_height": null,
-       "max_width": null,
-       "min_height": null,
-       "min_width": null,
-       "object_fit": null,
-       "object_position": null,
-       "order": null,
-       "overflow": null,
-       "padding": null,
-       "right": null,
-       "top": null,
-       "visibility": null,
-       "width": null
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "74899c00e43a4c4a9bb48656931d8fa0": {
+     "aa9f53f2be104bd4ba5701bb0e200b59": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "ProgressStyleModel",
@@ -6117,7 +6628,7 @@
        "description_width": ""
       }
      },
-     "793c3fefdcf44710b5b32d396931b1a0": {
+     "aad1c2c234aa4d9daff222197e874088": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "FloatProgressModel",
@@ -6133,17 +6644,17 @@
        "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_27d193479919478abccf42607a6b3032",
-       "max": 338,
-       "min": 0,
+       "layout": "IPY_MODEL_c139ead3f2574a94b13c3521f58ecf87",
+       "max": 37.0,
+       "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_4965af7bb8c74cb186d30e37d36ad8bd",
+       "style": "IPY_MODEL_a6579dc1c201438eba9931aec8fce315",
        "tabbable": null,
        "tooltip": null,
-       "value": 338
+       "value": 37.0
       }
      },
-     "7bd060f8373645e5aa91681002f1ed23": {
+     "aafbac41be004d218cfd0d32b779cc73": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6196,89 +6707,7 @@
        "width": null
       }
      },
-     "7c4e1be8f3894bada5435981f2062cc5": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_1dd3d7bbb92a4f2b84f86cf873e42fb7",
-        "IPY_MODEL_376884b55ebc4c15aaf378561c1839a3",
-        "IPY_MODEL_d05005e8c81e44cf8323376ba9b9b91c"
-       ],
-       "layout": "IPY_MODEL_6b265e7a4ffd45f2a28666521116af77",
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "8276e58154be4b79be454000483941ef": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_641cca32d5f04d0fa1be55d6fe2f7b72",
-        "IPY_MODEL_eae90241b29d499696eb2fb5184db062",
-        "IPY_MODEL_06b28508d49e45d29e2892bbbad9415c"
-       ],
-       "layout": "IPY_MODEL_1a5abea34e494dc683603229b7a65d73",
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "8999ebf7ab524d72aa6508e2d027ecc1": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
-      }
-     },
-     "8d81efaf768848c3a67b33245164f613": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "9242d318a92f41adae61428fd06845bd": {
+     "abf33312306a49968c759040af583034": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLStyleModel",
@@ -6296,7 +6725,7 @@
        "text_color": null
       }
      },
-     "92572de3c8bd4340920bd41041fa3677": {
+     "aca9b92fe28544429e3aeb37f953476a": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6349,25 +6778,7 @@
        "width": null
       }
      },
-     "9439aa1484394e07b5068134ee9ff6c8": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "94b84db8adb548058801c3a2298275a6": {
+     "aed07d02bd9a4211a7a263db3a433242": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLStyleModel",
@@ -6385,7 +6796,7 @@
        "text_color": null
       }
      },
-     "96c5aa840bc24103a3cde64f31f17190": {
+     "b00addf3a00c49fe856c02912bb81820": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6438,7 +6849,30 @@
        "width": null
       }
      },
-     "96e0801ba0654a9cab5448742b946d54": {
+     "b136c377d78946f599e1eb81b95942bc": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_763ddff7ad0f487e93ff6a28dc0147b8",
+       "placeholder": "​",
+       "style": "IPY_MODEL_1fd815879a3b492cae2307142253b678",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 37/37 [00:00&lt;00:00, 779.55 examples/s]"
+      }
+     },
+     "b1b7d133302c4d1999f22554d057ebda": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6488,10 +6922,10 @@
        "right": null,
        "top": null,
        "visibility": null,
-       "width": null
+       "width": "20px"
       }
      },
-     "97ee510d277147f89a1e7c919d9c32ee": {
+     "b1bf213038f644e88a82057010bc23df": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -6506,15 +6940,15 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_357f6d6b21f4431abf6cfdc934d33ee1",
+       "layout": "IPY_MODEL_d4aef6ffd13341a9a5d4d86c065c77e7",
        "placeholder": "​",
-       "style": "IPY_MODEL_9a3abb4cabdb49798b675d90172c1c6e",
+       "style": "IPY_MODEL_2ab08093b53a4cf184633770536b5e14",
        "tabbable": null,
        "tooltip": null,
-       "value": " 4/4 [00:00&lt;00:00, 301.38 examples/s]"
+       "value": "Tokenizing train dataset: 100%"
       }
      },
-     "9a3abb4cabdb49798b675d90172c1c6e": {
+     "b2024485da4c46ba9dad57b96cd97ef0": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLStyleModel",
@@ -6532,7 +6966,7 @@
        "text_color": null
       }
      },
-     "9fe4c015f8e64fa7bd1e12664f74dbcd": {
+     "b24eb59d45114caba4ccf8daf786fbbd": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -6547,41 +6981,78 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_b69577c0c1434894b7fc8ae9037cc940",
+       "layout": "IPY_MODEL_fab7957fa32c478395994bfcb704d422",
        "placeholder": "​",
-       "style": "IPY_MODEL_460b0c74040b4031a721d1768173940a",
+       "style": "IPY_MODEL_928d7051207b480b97cb3b0d283bde38",
        "tabbable": null,
        "tooltip": null,
-       "value": " 242/242 [00:00&lt;00:00, 52.8kB/s]"
+       "value": "Loading weights: 100%"
       }
      },
-     "a3e070d331ed46d9aeaeedb4c95b42fa": {
+     "b39e87fe3778477f909c1afeca1bf519": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
+      "model_name": "HBoxModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "FloatProgressModel",
+       "_model_name": "HBoxModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_5da72d1a02364074ab84e97896892343",
+        "IPY_MODEL_b8cdf9492e7640b8a64bc295ef80818a",
+        "IPY_MODEL_57e16ebbf2f74bde973dc7344c4b6628"
+       ],
+       "layout": "IPY_MODEL_50c654243e5240899af5b948b25722f7",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "b47891452c224617a69f1d71afdb4679": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "b6dea77b864f4f93927a54937c56a659": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_e042f5aea57943cdab80001c763cd0b1",
-       "max": 2776833,
-       "min": 0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_b5392d04a1714872b50ac74beb849411",
+       "layout": "IPY_MODEL_5df5558dde33430687056f9455ac6ede",
+       "placeholder": "​",
+       "style": "IPY_MODEL_b2024485da4c46ba9dad57b96cd97ef0",
        "tabbable": null,
        "tooltip": null,
-       "value": 2776833
+       "value": "model.safetensors.index.json: 100%"
       }
      },
-     "a4cc2830308b440bac5f69e757e5d511": {
+     "b71bc0388480429dbf7afd47116a9f7b": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6634,7 +7105,49 @@
        "width": null
       }
      },
-     "a4e984e6b37c48b28d84acbb7f5fa9d7": {
+     "b85d95f82e714d949c5a656cf022b712": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "b8cdf9492e7640b8a64bc295ef80818a": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_3a3e24a1bde7455eaf9d3c6b63cb0ac6",
+       "max": 2776833.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_b85d95f82e714d949c5a656cf022b712",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 2776833.0
+      }
+     },
+     "b92002ece3f442e4bc1098a2f94aa8a0": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6687,7 +7200,67 @@
        "width": null
       }
      },
-     "a7bc4fd218054751ba10ed47a5128b4a": {
+     "b9bd8a943e684e91971bbb9eeb365f93": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "ba498a2526854a97b4ad6131d4b476cf": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_b6dea77b864f4f93927a54937c56a659",
+        "IPY_MODEL_46037f140e0044449ab1e4dafa27de2c",
+        "IPY_MODEL_e9ba98a596e94ed89420def2de912d6b"
+       ],
+       "layout": "IPY_MODEL_5e292052f0e44ddfb7d8381a3851e369",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "ba9d2e4c10794a85a17a3b89c9f7ef27": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "bd453c2475754202b484bac5ed9f2b69": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6740,7 +7313,48 @@
        "width": null
       }
      },
-     "ac607682814e4e3a917668290a8e6172": {
+     "bd89fc79d671497f8c037f17a8d6122a": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "bfb2fb9a397a4bacaee8faa923521fa2": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_601fcf7b6b5244258dbf9153c387b9f8",
+       "placeholder": "​",
+       "style": "IPY_MODEL_f2ab12c59b59458587844d9c97cdc864",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 37/37 [00:00&lt;00:00, 4763.04 examples/s]"
+      }
+     },
+     "c139ead3f2574a94b13c3521f58ecf87": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6793,7 +7407,23 @@
        "width": null
       }
      },
-     "ace8e79cc1234b8abe7a85973a70dcf4": {
+     "c1d2af50ff284d2c9cd5b5c966825b00": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "c23456867d644af5bfc9d822b97964c5": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HBoxModel",
@@ -6808,16 +7438,16 @@
        "_view_name": "HBoxView",
        "box_style": "",
        "children": [
-        "IPY_MODEL_e0355e0fbce74bd1a52d05b04564f356",
-        "IPY_MODEL_2c2c1141de114958983f8669528f55ca",
-        "IPY_MODEL_97ee510d277147f89a1e7c919d9c32ee"
+        "IPY_MODEL_8b0c58c46ad04ee7bf421517b0a9773b",
+        "IPY_MODEL_aad1c2c234aa4d9daff222197e874088",
+        "IPY_MODEL_bfb2fb9a397a4bacaee8faa923521fa2"
        ],
-       "layout": "IPY_MODEL_ac607682814e4e3a917668290a8e6172",
+       "layout": "IPY_MODEL_f615273549414b8a9f0135f6d1dc1d69",
        "tabbable": null,
        "tooltip": null
       }
      },
-     "ae7ba26d724b433c96831c533ee5583e": {
+     "c45283202f3a4261891488f4e281ac22": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6870,7 +7500,7 @@
        "width": null
       }
      },
-     "af8273c61a184445b9ad558e2a88719a": {
+     "c52288ca86e94235b7d0038f8199891a": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "FloatProgressModel",
@@ -6886,33 +7516,17 @@
        "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_37f812ce5e2347479778ffccab06cb54",
-       "max": 7031645,
-       "min": 0,
+       "layout": "IPY_MODEL_b1b7d133302c4d1999f22554d057ebda",
+       "max": 1.0,
+       "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_74899c00e43a4c4a9bb48656931d8fa0",
+       "style": "IPY_MODEL_f38461c11ea6442cbaf52f000e1b7f80",
        "tabbable": null,
        "tooltip": null,
-       "value": 7031645
-      }
-     },
-     "b5392d04a1714872b50ac74beb849411": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
+       "value": 1.0
       }
      },
-     "b69577c0c1434894b7fc8ae9037cc940": {
+     "c5f692d0f4b8496c84da7bed309db3f7": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -6965,7 +7579,33 @@
        "width": null
       }
      },
-     "baf825cbfbfd4042b73a23d136986c7c": {
+     "c6375ffd3bf146d4b49e2b5f76d63df7": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_f9e45e7545a34dbd9a047351a020be90",
+       "max": 1671839.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_3bcb98cc398145bcaf9b73d09e028d1c",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 1671839.0
+      }
+     },
+     "c7a7063d0ba04c05b8e8b807c33e2a0c": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7018,7 +7658,53 @@
        "width": null
       }
      },
-     "bb03fd6868774ae1a8837a1b77ddca8b": {
+     "c989786e5799465d9d28c1f137b9e972": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_d1e13a8f7dfc4c0da91a0063e05770a6",
+       "placeholder": "​",
+       "style": "IPY_MODEL_a96bc65796884e14a457acabc61ac455",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 661/661 [00:00&lt;00:00, 141kB/s]"
+      }
+     },
+     "c9d1121116704df282befbbd64a0399c": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_59da4931c81446a495d8b4eceab2998b",
+       "placeholder": "​",
+       "style": "IPY_MODEL_89d9da8bd78e4b599ad360d5c589c70f",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 45/45 [00:00&lt;00:00, 734.17 examples/s]"
+      }
+     },
+     "c9e6ad722fbe46c7a25d0eccd6d29cfe": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7071,7 +7757,33 @@
        "width": null
       }
      },
-     "bf872f9dac41450bb3758bc6720d6ce9": {
+     "cb1761b73e964e6c9f7fbe53a651d963": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_d212739b6136459b91bde0eedc823356",
+       "max": 242.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_4c08c2d5f4ee4f178435afd243c30878",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 242.0
+      }
+     },
+     "cb8d7770349d4f3695b0b378fa8868e5": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7124,84 +7836,157 @@
        "width": null
       }
      },
-     "c5338b61e2cc4f3d868ba2370a96b50c": {
+     "cbacef012d9e46a5bb65ad4b84216b13": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
+      "model_name": "HTMLModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
+       "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_2bf895071502432d8e9fa99c4c16723e",
+       "placeholder": "​",
+       "style": "IPY_MODEL_013c2f294e10458f9c812fe267dac401",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 45/45 [00:00&lt;00:00, 768.25 examples/s]"
       }
      },
-     "c70a5078ca8d4c32a4100fb3fc60853a": {
+     "cc826df8445d4a55b2b15ec2298a3841": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
+      "model_name": "FloatProgressModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "FloatProgressModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_f2dc1d2b0dba40af83d87522292b5bd6",
-       "placeholder": "​",
-       "style": "IPY_MODEL_8d81efaf768848c3a67b33245164f613",
+       "layout": "IPY_MODEL_b71bc0388480429dbf7afd47116a9f7b",
+       "max": 38.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_c1d2af50ff284d2c9cd5b5c966825b00",
        "tabbable": null,
        "tooltip": null,
-       "value": "merges.txt: 100%"
+       "value": 38.0
       }
      },
-     "c89c6918ae5f463eae3cf48b41f913b5": {
+     "cca2d67214454b03bd25edbcf9b3f520": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
+      "model_name": "HBoxModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
+       "_model_name": "HBoxModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
+       "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_963fcac71d454a1c9929499535cbeeea",
+        "IPY_MODEL_cb1761b73e964e6c9f7fbe53a651d963",
+        "IPY_MODEL_4b5fc2522a094c9d9d0d3e239f92b557"
+       ],
+       "layout": "IPY_MODEL_3208b1140d444cd49050a8aed9295f94",
+       "tabbable": null,
+       "tooltip": null
       }
      },
-     "cbb206a606ed44229ac17289f17ed9c3": {
+     "cf10a5c9efc5488f9bc3a88e14f8d75d": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
+      "model_name": "HBoxModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_2e022e459d974bb3a16191809edc7678",
+        "IPY_MODEL_ede8b5671106480dad6daafa70e54d26",
+        "IPY_MODEL_609d81288c1f40d994bb5360ba3c6f9e"
+       ],
+       "layout": "IPY_MODEL_77aa49df8e5e43dcbe3140c8bb2a01b1",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "d1e13a8f7dfc4c0da91a0063e05770a6": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "cbe219db8818475cb825daf8a3d3e838": {
+     "d212739b6136459b91bde0eedc823356": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7254,7 +8039,7 @@
        "width": null
       }
      },
-     "cce612ba0d474798946245ab85019b4e": {
+     "d23772c65d33402c91f15e39fc26893c": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7307,7 +8092,25 @@
        "width": null
       }
      },
-     "ce107e335c1e41a9b9d0aeafb578c4b8": {
+     "d259afd5fff541b5b5c9703c437f1027": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "d4aef6ffd13341a9a5d4d86c065c77e7": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7360,7 +8163,7 @@
        "width": null
       }
      },
-     "d05005e8c81e44cf8323376ba9b9b91c": {
+     "d4be0d5754174b68b1846237af1ffad4": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -7375,57 +8178,15 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_dcc4ae7d4d9844e5b70e5c57bbe1590e",
+       "layout": "IPY_MODEL_6b4fde19aab54b24a186e4a69bb5fa1a",
        "placeholder": "​",
-       "style": "IPY_MODEL_4ed3d5a4a24e404996782992ce69cfd2",
+       "style": "IPY_MODEL_bd89fc79d671497f8c037f17a8d6122a",
        "tabbable": null,
        "tooltip": null,
-       "value": " 7.30k/7.30k [00:00&lt;00:00, 1.69MB/s]"
-      }
-     },
-     "d6dad142bf7a4c769012c057db8e12db": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "d7f98669d5e64269a1461e9b151aa312": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_603f6b5d27264d49a3070989a413ee83",
-        "IPY_MODEL_a3e070d331ed46d9aeaeedb4c95b42fa",
-        "IPY_MODEL_3bdc93fafa724bdcb00b515e1b33332b"
-       ],
-       "layout": "IPY_MODEL_3adecb27e83942588088da579cb170b2",
-       "tabbable": null,
-       "tooltip": null
+       "value": "merges.txt: 100%"
       }
      },
-     "da32983d4d164c4a942e1fff2b234113": {
+     "d535d411f17440319f027abce44f4486": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7478,7 +8239,51 @@
        "width": null
       }
      },
-     "dcc4ae7d4d9844e5b70e5c57bbe1590e": {
+     "d6cb204c4cbe408890c4faf682a80558": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "d7a5dfeae2c740d88a90b484109d8e2c": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_86e221d182824762992d512c1c2f8b18",
+       "max": 45.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_aa9f53f2be104bd4ba5701bb0e200b59",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 45.0
+      }
+     },
+     "dd5d8309949b46209ca763fab0f345f0": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7531,7 +8336,257 @@
        "width": null
       }
      },
-     "dcd14169f60b488ab3d3fcd58ed29b27": {
+     "ddbf9647c7a648bc9085b7bbd2b0c8c6": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_f4ac2749f86c45bf88a49f3d4f78ea9f",
+       "placeholder": "​",
+       "style": "IPY_MODEL_aed07d02bd9a4211a7a263db3a433242",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "Adding EOS to train dataset: 100%"
+      }
+     },
+     "de81e605c1744caa93f503dfa0c58bf7": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_e7ece920797144ecb0bda3f3b45b5689",
+        "IPY_MODEL_a020504b1e7a415fa1bbdac38aba88a8",
+        "IPY_MODEL_cbacef012d9e46a5bb65ad4b84216b13"
+       ],
+       "layout": "IPY_MODEL_fb403d9aaa7749b4bc46b05e876b5c53",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "de9bb66f5c234904b57c3d68290fb67f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_d535d411f17440319f027abce44f4486",
+       "max": 661.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_b47891452c224617a69f1d71afdb4679",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 661.0
+      }
+     },
+     "e1f35d1cd78449aea09331ab27fef1f0": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_462b2d545c07419a89f23aa4fdec943d",
+       "placeholder": "​",
+       "style": "IPY_MODEL_d259afd5fff541b5b5c9703c437f1027",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 6.17G/6.17G [01:34&lt;00:00, 1.03GB/s]"
+      }
+     },
+     "e35e0481b51d44c68d7c2b0dac91899d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "e4ccd21c137e4c5f87b908a17752a7df": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_b1bf213038f644e88a82057010bc23df",
+        "IPY_MODEL_a233ffa1cba6416a95e82eefac5e132c",
+        "IPY_MODEL_0a9867373b3a40179290534593d1867e"
+       ],
+       "layout": "IPY_MODEL_1d08b23ba7c744c7a49acfff2c7413f1",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "e580c60c036540b1a405210346088404": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_6265673735ab47bca14d5cec6f258f5f",
+        "IPY_MODEL_7f1f7f62515f4d219a552c55179c7c39",
+        "IPY_MODEL_3ac78502b3d64574846c856320361fb5"
+       ],
+       "layout": "IPY_MODEL_9e108ca645ea44bfa3fd92bae857bd14",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "e7ece920797144ecb0bda3f3b45b5689": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_40af92436a5b4fb1b2be07178c408c26",
+       "placeholder": "​",
+       "style": "IPY_MODEL_e91610eff16043faad7ad2b39cd60478",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "Tokenizing train dataset: 100%"
+      }
+     },
+     "e91610eff16043faad7ad2b39cd60478": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "e9ba98a596e94ed89420def2de912d6b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_00bc8d66e36a4050ae315c58b2e230aa",
+       "placeholder": "​",
+       "style": "IPY_MODEL_abf33312306a49968c759040af583034",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 35.6k/35.6k [00:00&lt;00:00, 7.85MB/s]"
+      }
+     },
+     "ede8b5671106480dad6daafa70e54d26": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_cb8d7770349d4f3695b0b378fa8868e5",
+       "max": 2.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_f3998008edba438f8d14934be6de79fe",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 2.0
+      }
+     },
+     "f149f0e7f0ed4ef6804e532c36b99eb9": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7584,7 +8639,7 @@
        "width": null
       }
      },
-     "e0355e0fbce74bd1a52d05b04564f356": {
+     "f2101446750d49aa8111e6dc1270ba48": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -7599,15 +8654,65 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_1444f5ba0f92425d81dc3aea332fdfc8",
+       "layout": "IPY_MODEL_d23772c65d33402c91f15e39fc26893c",
        "placeholder": "​",
-       "style": "IPY_MODEL_387d6d2981a74ab3abbea7b352fbfb39",
+       "style": "IPY_MODEL_98551ddb2ded458b87fb0e346b77a2b3",
        "tabbable": null,
        "tooltip": null,
-       "value": "Tokenizing train dataset: 100%"
+       "value": " 434/434 [00:00&lt;00:00, 845.65it/s]"
+      }
+     },
+     "f2ab12c59b59458587844d9c97cdc864": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
+      }
+     },
+     "f38461c11ea6442cbaf52f000e1b7f80": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "f3998008edba438f8d14934be6de79fe": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
       }
      },
-     "e042f5aea57943cdab80001c763cd0b1": {
+     "f4ac2749f86c45bf88a49f3d4f78ea9f": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7660,7 +8765,7 @@
        "width": null
       }
      },
-     "e338db5125d24f57a0e58d905b1aacb0": {
+     "f615273549414b8a9f0135f6d1dc1d69": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7713,7 +8818,7 @@
        "width": null
       }
      },
-     "e6bf97b928c240afa63326920624d9f8": {
+     "f9e45e7545a34dbd9a047351a020be90": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7766,142 +8871,23 @@
        "width": null
       }
      },
-     "ea9c9055255943aea8dd542e48b91266": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "eae90241b29d499696eb2fb5184db062": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "FloatProgressModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_dcd14169f60b488ab3d3fcd58ed29b27",
-       "max": 660,
-       "min": 0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_443cc98027db4b7d88e561a442945c2d",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 660
-      }
-     },
-     "ebb37c4d91bf4a42a7aef1e8186062e9": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "FloatProgressModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_330301478152410481945768d907f057",
-       "max": 242,
-       "min": 0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_28fbc46f8dc44a6c9c2e71ab97d58c02",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 242
-      }
-     },
-     "ef27ba1ee51a46d6a5790f9623d59ac6": {
+     "fa037963a762456f9059c4924d070a4d": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
+      "model_name": "ProgressStyleModel",
       "state": {
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
+       "_model_name": "ProgressStyleModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
        "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "f007d918dc6147869c69cd3a5bd3d2e5": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_1aab4e7be84b41e0bb4c3c5d1078bd88",
-       "placeholder": "​",
-       "style": "IPY_MODEL_94b84db8adb548058801c3a2298275a6",
-       "tabbable": null,
-       "tooltip": null,
-       "value": " 1.67M/1.67M [00:00&lt;00:00, 33.5MB/s]"
-      }
-     },
-     "f0d3058737d44ef98442b1576d0626e1": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_42e7c29f98674f64a64c3abdb9b40679",
-        "IPY_MODEL_6e874941b66a44919079f959eae61fc2",
-        "IPY_MODEL_46671a6dad1f44fd92c451cdbffa3214"
-       ],
-       "layout": "IPY_MODEL_cce612ba0d474798946245ab85019b4e",
-       "tabbable": null,
-       "tooltip": null
+       "bar_color": null,
+       "description_width": ""
       }
      },
-     "f2dc1d2b0dba40af83d87522292b5bd6": {
+     "fab7957fa32c478395994bfcb704d422": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -7954,25 +8940,7 @@
        "width": null
       }
      },
-     "f3eb296230564402b0171e5d638d2037": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "f4662c56e2a8457ca7398fee9bc60295": {
+     "fb403d9aaa7749b4bc46b05e876b5c53": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -8025,72 +8993,7 @@
        "width": null
       }
      },
-     "f5cb210961614af3a2c0372c8dd5427f": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "f69304571dcf4176b37a7bf5fbff895c": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_52508674e4c048d9a7e7e8f54af2d737",
-        "IPY_MODEL_af8273c61a184445b9ad558e2a88719a",
-        "IPY_MODEL_47358f9f932a40a8ba48961ef65d630b"
-       ],
-       "layout": "IPY_MODEL_baf825cbfbfd4042b73a23d136986c7c",
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "f9aa2255c1094523a28e4e5850013ff3": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_bb03fd6868774ae1a8837a1b77ddca8b",
-       "placeholder": "​",
-       "style": "IPY_MODEL_9439aa1484394e07b5068134ee9ff6c8",
-       "tabbable": null,
-       "tooltip": null,
-       "value": "Loading weights: 100%"
-      }
-     },
-     "fb8ad84b44544cbea03e46d27758b6f6": {
+     "fca11b43c8fd492689df1a22840f6b22": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -8151,4 +9054,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file